Bionics Chemical Synapse

Surachoke Thanapitak

December 2011

A thesis submitted for the degree of Doctor of Philosophy of Imperial College London

Department of Electrical and Electronic Engineering Imperial College of Science, Technology and Medicine Acknowledgements

First of all, I would like to express my gratitude to Professor Chris Toumazou who has been my supervisor for the past ﬁve years, ever since I was an MSc student in 2006. Without his support and encouragement, this work would not have reached a successful conclusion. Professor Toumazou not only inspired my interest in analogue circuit design but he has also enlightened me to understand how important it is especially in the ﬁeld of bionics.

Secondly, I am grateful to my fellow researchers at the Centre of Bio-inspired Technol- ogy and other groups, including Dr. Panavy Pookaiyaudom, Dr. Thanut Tosanguan, Jakgrarath Leenutaphong, Yan Liu, Abdul Al-ahdal, Achirapa Bandhaya, Jackravut Dejvises, Supattra Visessri, Soratos Tantideeravit, Sasinee Bunyarataphan and Parinya Seelanan. Also, I would like to thanks Dr. Timothy Constandinou, Dr. Pantelis Geor- giou, Dr. Amir Eftekhar and Dr. Themistoklis Prodromakis for all of their helpful advice and support through out the period of my PhD studentship.

The Royal Thai Government is an organisation which I feel deeply indebted for their support throughout my study in the UK. Without the financial support from the Royal Thai Government, I would not have been able to study at Imperial College. Also, I would like to thank the Office of Educational Affairs (OEA) for looking after me throughout my stay in London.

Finally, this work would not have been completed without the love and kind support from my family back home in Chiang Mai, Thailand.

i To the king, country and my parents.

ii ”No matter how much you think, you won’t know. Only when you stop thinking will you know. But still, you have to depend on thinking so as to know.”

From ”Gifts He Left Behind: The Dhamma Legacy of Ajaan Dune Atulo”, compiled by Phra Bodhinandamuni, translated from the Thai by Thanissaro Bhikkhu. Access to Insight, 16 June 2011, http://www.accesstoinsight.org/lib/thai/dune/giftsheleft.html . Retrieved on 7 November 2011.

iii Abstract

This thesis presents the very ﬁrst bionics chemical synapse which has the capability to sense the neurotransmitter (glutamate) and imitates the physiological behaviour of certain chemical synapse receptors (i.e. AMP A, NMDA, GABAA and GABAB). This bionics chemical synapse consists of two main parts: the glutamate ISFETs that act as neurotransmitter sensors and the current-mode CMOS circuits that have been designed to match the physiological behaviour of the chemical synapses.

This bionics chemical synapse requires a sub-nano Siemens operational transconductance am- pliﬁer (OTA) to develop a low conductance gain for each chemical synapse receptor (0.1nS). A combination of two OTA designs was required to decrease the overall transconductance gain, which were: the bulk driven transistor and the drain current normalisation.

To create the bionics chemical synapse, a neurotransmitter sensor is required as the chemical front-end for each receptor circuit. The sensor that was used is an enzyme-modiﬁed ISFET with glutamate oxidase immobilisation, to make the ISFET sensitive to glutamate ions. Additionally, a fast chemical perturbation technique called iontophoresis was applied to generate the glutamate stimulus, which represents the neurotransmitter signal. This signal has a one millisecond time duration.

Finally, the current-mode CMOS circuits biased in the weak inversion region have been designed to match a biological model of the four mentioned chemical synapse receptors. Circuit techniques, such as the log domain ﬁlter and the translinear loop, were applied to realise the complex mathematical functions in the chemical synapse model. The measured response of the fabricated AMP A and NMDA receptors, where the glutamate ISFET was used to sensed the artiﬁcial neurotransmitter stimulus, closely matches with the circuit simulation results.

iv Abbreviations and Acronyms

AMPA Alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid BJT Bipolar junction transistor CNS Central nervous system CMOS Complementary metal oxide semiconductor CNBH Cynaoborohydride EnFET Enzyme field effect transistor GABA Gamma-aminobutyric acid GluOX Glutamate oxidase ISFET Ion sensitive field effect transistor KCL Kirchhoff’s circuit laws LPeD1 Left pedal dorsal 1 MOSFET Metal oxide field effect transistor NMDA N-Methyl-D-aspartic acid OTA Operational transconductance amplifier PECVD Plasma enhanced chemical vapour PBS Phosphate Buffer Saline PLL Poly-l-lysine REFET Reference field effect transistor SCI Spinal cord injury VD4 Visceral dorsal 4 VLSI Very large scale integration

vi Contents

Acknowledgements i

Abstract iv

1 Introduction 1

1.1 Motivation ...... 1

1.2 Research Objective ...... 2

1.3 Overview ...... 3

1.3.1 Silicon Neuromorphic ...... 4

1.3.2 Low-gain OTA design ...... 5

1.3.3 ISFET and Iontophoresis Technique ...... 5

1.3.4 Bio-inspired Chemical Synapse ...... 6

2 Silicon Neuromorphic 9

2.1 Introduction ...... 9

2.2 Neuron ...... 10

vii CONTENTS viii

2.2.1 Ion channels and electrical properties of membranes ...... 11

2.2.2 Nernst Equation ...... 12

2.3 Action potential and its model ...... 13

2.3.1 Generation of Action Potential ...... 13

2.3.2 Membrane potential model ...... 14

2.3.3 Hodgkin and Huxley model ...... 17

2.3.4 Single Compartment ...... 23

2.4 The Synapse ...... 28

2.4.1 Electrical Synapse ...... 28

2.4.2 Chemical Synapse ...... 28

2.4.3 Biological model for the chemical synapse ...... 30

2.4.4 Postsynaptic simulation ...... 36

2.5 Silicon neuromorphic circuits ...... 42

2.5.1 Silicon Neurons ...... 42

2.5.2 Silicon Synapses ...... 44

2.6 Summary ...... 46

3 Low-gain OTA design 53

3.1 Introduction ...... 53

3.2 Analysis of diﬀerential pair as an OTA ...... 54

3.3 Analysis with signal ﬂow graph technique ...... 56 CONTENTS ix

3.4 Analysis of a bulk driven OTA with source degeneration and bump linearisation ...... 60

3.5 Analysis of double diﬀerential pair OTA ...... 62

3.6 Summary ...... 69

4 ISFET and Iontophoresis Technique 72

4.1 Introduction ...... 72

4.2 ISFET principle ...... 73

4.2.1 ISFET Operation ...... 74

4.2.2 ISFET sensitiviy ...... 76

4.2.3 Reference electrode ...... 80

4.2.4 Drift in ISFET ...... 82

4.3 Enzyme-Immobilised ISFET ...... 83

4.3.1 Glutamate ISFET ...... 84

4.4 Coulometric titration ...... 92

4.5 Iontophoresis method ...... 95

4.6 Experimental results on Iontophoresis ...... 99

4.7 Summary ...... 103

5 Bio-inspired Chemical Synapse 110

5.1 Introduction ...... 110

5.2 Neural bridge ...... 112 CONTENTS x

5.2.1 Non-invasive neuron stimulus ...... 113

5.2.2 Hippocampal neural bridge ...... 113

5.3 Implementation of chemical synapse receptor ...... 115

5.3.1 AMP A receptor ...... 115

5.3.2 NMDA receptor ...... 117

5.3.3 GABAA receptor ...... 119

5.3.4 GABAB receptor ...... 121

5.4 Implementation of the postsynaptic transmission ...... 124

5.4.1 Postsynaptic circuit for the AMP A receptor ...... 126

5.4.2 Postsynaptic circuit for the NMDA receptor ...... 129

5.4.3 Postsynaptic circuit for the GABAA receptor ...... 134

5.4.4 Postsynaptic circuit for the GABAB receptor ...... 136

5.5 Summary ...... 142

6 Conclusion and Future Work 146

6.1 Contribution ...... 147

6.2 Recommendation for Future Work ...... 149

6.2.1 Integration of the components on the same chip ...... 149

6.2.2 The non-invasive and direct extracellular glutamate detector . . 150

6.2.3 Live neuron experiment ...... 150

A Publications 154 B PCB outline of Bionics Chemical Synapse 155

xi List of Tables

2.1 Hodgkin and Huxley nerve axon model parameters ...... 23

2.2 Transformed Hodgkin and Huxley axon model parameters ...... 23

2.3 Summary Properties of Synapses ...... 31

3.1 Important parameters of each OTAs design ...... 69

4.1 Common analytes and immobilised enzymes used in EnFET ...... 84

4.2 Data of the measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from a voltage-mode readout circuit [30] ...... 86

4.3 Data of the measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.44V ...... 89

4.4 Data of the measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.26V ...... 90

4.5 Data of the measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when

Vref = 0.21V ...... 91

xii 5.1 AMP A, NMDA, GABAA and GABAB parameters ...... 141

xiii List of Figures

1.1 Chemical synapse (a) and Electrical circuit implement (b) ...... 4

2.1 The structure of a neuron ...... 11

2.2 Typical Nerve Action Potential ...... 15

2.3 Equivalent electrical circuit for the Hodgkin-Huxley model ...... 18

2.4 Rate constants (a) αn and (b) βn ...... 24

2.5 Activation of potassium channel (n)...... 25

2.6 Rate constants (a) αm and (b) βm ...... 25

2.7 Activation of the sodium channel (m)...... 26

2.8 Rate constant (a) αh and (b) βh ...... 26

2.9 Inactivation of the sodium channel (h)...... 27

2.10 (a) The action potential (vm) observed when applied with (b) the total

membrane current (Im)...... 27

2.11 Electrical synapse ...... 29

2.12 Chemical synapse ...... 30

xiv LIST OF FIGURES xv

2.13 MATLAB simulation of rAMP A ...... 33

2.14 MATLAB simulation of rNMDA ...... 34

2.15 MATLAB simulation of rGABAA ...... 35

2.16 MATLAB simulation of rGABAB ...... 36

2.17 (a) Single spike of AMP A neurotransmitter and (b) its postsynaptic response ...... 38

2.18 (a) Four spikes of AMP A neurotransmitter and (b) its postsynaptic response ...... 38

2.19 (a) Single spike of NMDA neurotransmitter and (b) its postsynaptic response ...... 39

2.20 (a) Four spikes of NMDA neurotransmitter and (b) its postsynaptic response ...... 39

2.21 (a) A single spike of GABAA neurotransmitter and (b) its postsynaptic response ...... 40

2.22 (a) Four spikes of GABAA neurotransmitter and (b) its postsynaptic response ...... 40

2.23 (a) Ten spikes of GABAB receptor and (b) its postsynaptic response . . 41

2.24 Hodgkin and Huxley implementation on CMOS of Toumazou et al. [29] 43

2.25 r implementation with a Bernoulli cell by Lazaridis et al. [33] ...... 45

2.26 Gordon’s synapse circuit ...... 46

3.1 A diﬀerential pair transconductance ampliﬁer ...... 54 LIST OF FIGURES xvi

3.2 (a) A transistor with corresponding voltages and currents. (b) The small signal equivalent circuit for the bulk transistor. (c) The signal ﬂow graph of dimensionless model for the bulk transistor...... 57

3.3 Diﬀerential pair as an OTA with double source degeneration ...... 58

3.4 (a) The half equivalent circuit of OTA in Fig.(3.3). (b) The signal ﬂow graph of Fig.(3.4(a)) ...... 58

3.5 Simulation result for transconductance ampliﬁer in Fig.(3.3) ...... 59

3.6 Bulk diﬀerential pair as an OTA with double source degeneration . . . . 60

3.7 (a) The half equivalent circuit of OTA in Fig.(3.6). (b) The signal ﬂow graph of Fig.(3.7(a)) ...... 61

3.8 Simulation result between output current and diﬀerential input voltage of the OTA in Fig.(3.6) ...... 63

3.9 Variable linear range OTA of S.P. DeWeerth et al...... 64

3.10 Half circuit of the inner diﬀerential pair of Fig.(3.9) ...... 65

3.11 The double diﬀerential pair OTA ...... 66

3.12 (a) The half equivalent circuit of OTA in Fig.(3.11). (b) The signal ﬂow graph of Fig.(3.12(a)) ...... 67

3.13 Simulation result between output current and diﬀerential input voltage of the OTA in Fig.(3.11) ...... 68

4.1 Ion Sensitive Field Eﬀect Transistor ...... 75

4.2 Drain current vs. reference electrode potential compared to ground for diﬀerent pH values ...... 76 LIST OF FIGURES xvii

4.3 Ag-AgCl reference electrode ...... 81

4.4 Measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from a voltage-mode readout circuit [30] ...... 87

4.5 Current mode ISFET readout circuit which exhibits a linear relationship between the output current and the concentration of analyte ...... 88

4.6 Measured results for diﬀerent HCL concentration of 0.5, 1, 1.5, 2 and

2.5mM from the current mode readout circuit in [31] when Vref = 0.44V 89

4.7 Measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2

and 2.5mM from the current mode readout circuit in [31] when Vref = 0.26V 90

4.8 Measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2

and 2.5mM from the current mode readout circuit in [31] when Vref = 0.21V 91

4.9 Diagram of coulometric titration ...... 93

4.10 Diagram of iontophoresis ...... 95

4.11 System used for iontophoresis experiment ...... 100

4.12 Measured result for three diﬀerent injected amplitudes at 1µm distance between the micropipette tip and the ISFET’s surface (insert is a ’Zoom in’ of one period of the measured result) ...... 101

4.13 Measured result for three diﬀerent current pulse widths at a ﬁxed injected amplitude of 1uA and a 1µm distance between the micropipette tip and the ISFET’s surface (insert is a ’Zoom in’ of one period of the measured result) ...... 102

5.1 Postsynaptic current of (A) AMP A receptor, (B) NMDA receptor, (C)

GABAA receptor and (D) GABAB receptor [4] ...... 111 LIST OF FIGURES xviii

5.2 A diagram based on Kaul’s experiment ...... 113

5.3 A circuit diagram for replacing a dysfunction central brain region with a VLSI system ...... 114

5.4 Diagram of the trisynaptic circuit of the hippocampus ...... 114

5.5 Conceptual representation of replacing the CA3 with a VLSI model . . . 115

5.6 Bernoulli cell circuit used for implementing variable rAMP A ...... 116

5.7 Bernoulli cell circuit used for implementing variable rNMDA ...... 118

5.8 Sigmoid circuit for B(V ) implementation ...... 119

5.9 Bernoulli cell circuit used for implementing variable r ...... 120 GABAA

5.10 Bernoulli cell circuit used for implementing variables r and u ... 122 GABAB

5.11 Translinear current multiplication circuit ...... 123

u4 5.12 Circuit implementation of function 4 ...... 124 u +Kd

5.13 Circuit of the bionics postsynaptic chemical synapse ...... 126

5.14 Low transconductance gain OTA circuit ...... 127

5.15 Measured vs. simulation results for the AMP A receptor ...... 129

5.16 Full schematic of a Bionics chemical synapse for the AMPA receptor . . 130

5.17 Measured vs. simulation results for the NMDA receptor ...... 132

5.18 Full schematic of a Bionics chemical synapse for the NMDA receptor . . 133

5.19 Measured vs. simulation results for the GABAA receptor ...... 135

5.20 Full schematic of a Bionics chemical synapse for the GABAA receptor . 136 5.21 Measured vs. simulation results for the GABAB receptor ...... 138

5.23 Microphotograph of the fabricated chemical synapse ...... 138

5.22 Full schematic of a Bionics chemical synapse for the GABAB receptor . 139

5.24 The photograph of bionics chemical synapse chip test and application board ...... 140

5.25 Experimental setup for bionics chemical synapse chip ...... 140

5.26 Closed up picture of the glutamate ISFET and the tip of the micropipette141

B.1 PCB schematic for a bionics chemical synapse chip ...... 156

B.2 PCB schematic for the OPAMP buﬀer and BNC, SMA ports ...... 157

B.3 PCB schematic for the BNC, SMA ports I ...... 158

B.4 PCB schematic for the BNC, SMA ports II ...... 159

xix Chapter 1

Introduction

1.1 Motivation

In the past decade, digital electronics seems to have dominated in all aspects of the electronics industry while analogue electronics appears to have faded away. However, analogue electronics has flourished in the field of neuromorphic engineering, first proposed by Mead in the late 1980s. Neuromorphic engineering has been applying analogue electronics, which has the capability to process signals in real-time and at the same time consume very little power, to emulate the models of neural systems.

The idea of a direct neural interface between a silicon chip and neural cells has been progressively studied since the ﬁrst neurochip was proposed by Maher et al. in 1998

[1]. Maher’s neurochip has the ability to both record and stimulate cultured neurons with the same sensor. In 2005, DeMarse et al. presented a very interesting work where cultured rat neural networks were trained to control a ﬁghter aircraft, via an electrode array, in a ﬂight simulator [2]. These examples demonstrate the possibility of using

1 1.2. Research Objective 2 electronics circuit to interface with live neurons.

Spinal cord injury (SCI) refers to the damage of the spinal cord from a body wound or shock, which causes loss of movements and sensations that may have resulted from axon or synapse degeneration in the central nervous system (CNS). Research on the medical treatment of SCI mainly focuses on the regeneration of neurons by applying neuroregenerative substances to the damage area of the spinal cord [3, 4]. In the CNS, glutamate is the vital neurotransmitter that has an important role in rapid synaptic transmission. Implementation of an artiﬁcial glutamate receptor to detect extracellular glutamate at the spinal cord could prove to be a useful alternative method for SCI treatment.

The inspiration of this thesis is the possibility of using an artificial chemical synapse to cure patients who suffer from spinal cord injury or paralysis by reconnecting the damaged neural signal paths. The feasibility of this approach was demonstrated by Berger et al., where a neuro-biomimetic silicon chip was used as a replacement neuron in the hippocampus [5]. Berger’s chip was designed to match the behaviour of a CA3 neuron in the hippocampal region. These in-vitro experiments of neural prostheses motivate the author to use an artificial device, i.e. electronic circuits, to mimic the physiological function of neurons and bypass the damaged neural path.

1.2 Research Objective

The objective of this research is to develop an artiﬁcial synapse that would not only duplicate the function of chemical synapses but also has the capability to sense the actual 1.3. Overview 3 neurotransmitter concentration change. This synthetic synapse is aimed at patients with spinal cord injury where it can be potentially used for the re-connection of the damaged neural pathway. To achieve this, there are two essential topics that needs to be investigated in this thesis:

1. The complexity of the chemical synapse model and the electronic circuit’s ability to

emulate it. The chemical synapse is modelled by a set of complex mathematical

functions [6] i.e. the ﬁrst order diﬀerential equation, the sigmoid function and

the fourth power function. Therefore, suitable electronic circuits are required to

reproduce this behaviour while maintaining low power consumption for biomedical

application.

2. Sensing the chemical concentration in Molar unit vs. traditional ISFET readout

circuits. In a chemical synapse model, the neurotransmitter release is a brief

pulse of 1 mM in amplitude and 1ms in duration [6]. The traditional ISFET

readout circuit has a logarithmic relationship with concentration [7]. Furthermore,

a very fast chemical titration technique is required to generate the one millisecond

neurotransmitter test signal.

1.3 Overview

A chemical synapse in Fig.(1.1(a)) can be functionally transformed into an electronic circuit called the Bionics Chemical Synapse, shown in Fig.(1.1(b)). In this work, the bionics chemical synapse has been successfully implemented on an integrated circuit with a separate or oﬀ-chip ISFET chemical sensor. This integrated circuit in CMOS technology was designed according to Destexhe’s mathematical model of the chemical synapse [6] and acts as the processing circuit, while the ISFET chemical sensor (ISFET) 1.3. Overview 4 operates as a neurotransmitter detector.

presynaptic cell action potential

Signal Processor postsynaptic cell Chemical postsynaptic Sensor Postsynaptic potential output (a) (b)

Figure 1.1: Chemical synapse (a) and Electrical circuit implement (b)

A brief description of each chapter in this thesis is as follows:

1.3.1 Silicon Neuromorphic

The basic concept of neurons and the idea of bio-inspired neural systems are presented in this chapter. Three important topics related to the physiology of the nervous systems - the neuron, the action potential and the synapse are described in detail. The action potential models based on diﬀerent mathematical functions are also examined, especially for the Hodgkin and Huxley model [8] where its simulation results in MAT-

LAB are shown. Furthermore, the chemical synapse based on the Destexhe model [6] is demonstrated with its simulation results. Finally, the silicon neuromorphic systems based on the mathematical models of neurons and synapses are reviewed. 1.3. Overview 5

1.3.2 Low-gain OTA design

For the Destexhe’s chemical synapse model, a sub-nano Siemens transconductor is required where the synapse’s conductance gain is 0.1nS. The chapter begins with an insightful analysis and explanation of an ordinary diﬀerential pair OTA. The macro model analysis technique for MOSFET circuits is described for complex OTA circuits.

Both, the body input and drain current normalisation OTAs are analysed via this macro model approach. In this chapter, a novel operational transconductance ampliﬁer (OTA) design, a combination of two transconductance ampliﬁer topologies: the body input [9] and the drain current normalisation [10], is presented. The fabricated OTA achieved a 0.1nS transconductance gain, which is in agreement with the calculation and the simulation result.

1.3.3 ISFET and Iontophoresis Technique

The principle of the ISFET is explained at the beginning of this chapter. The important properties of the ISFET are described, including the ISFET’s operation, sensitivity, and drift. Examples of the enzyme-immobilised ISFET are also given, with a detailed immobilisation procedure for the glutamate ISFET outlined. The experimental result on the non-linear characteristic of the traditional voltage-mode ISFET readout circuit

[7] to the ion concentration is shown. This non-linear relationship was overcome by using a current-mode ISFET readout circuit [11]. Finally, the iontophoresis technique that is capable of providing a fast ionic stimulus is introduced. This fast ionic perturbation represents the change in neurotransmitter concentration in Destexhe’s chemical synapse model [6]. 1.3. Overview 6

1.3.4 Bio-inspired Chemical Synapse

In this chapter, the CMOS circuit implementation of the chemical synapse based on the

Destexhe’s model [6] is presented. Initially, the idea of a neural bridge to reconnect the damaged neural pathway is introduced with two examples of neuron-electronic circuit interface experiments. Each receptor i.e. AMP A, NMDA, GABAA and GABAB, was formulated in the weakly inverted CMOS integrated circuit. The mathematical functions of each receptor were realised with current-mode circuit techniques, for instance: the Bernoulli cell for the ﬁrst order diﬀerential equation, the OTA for the conductance gain and the fourth power function by the translinear loop circuit. Finally, the glutamate ISFET that functions as the neurotransmitter sensor, was connected with the

AMP A and NMDA synapse circuits to form the full chemical synapse circuit. How- ever, due to the scarce availability of GABA oxidase to develop the GABA ISFET, the

GABAA and GABAB chemical synapse circuits were veriﬁed electronically. References

[1] M. Maher, J. Wright, J. Pine, and Y.-C. Tai, “A microstructure for interfacing with

neurons: the neurochip,” in Engineering in Medicine and Biology Society, 1998.

Proceedings of the 20th Annual International Conference of the IEEE, vol. 4, 1998,

pp. 1698–1702 vol.4.

[2] T. B. DeMarse and K. P. Dockendorf, “Adaptive ﬂight control with living neuronal

networks on microelectrode arrays,” in Neural Networks, 2005. IJCNN ’05. Pro-

ceedings. 2005 IEEE International Joint Conference on, vol. 3, 2005, pp. 1548–1551

vol. 3.

[3] A. R. Alexanian, M. G. Fehlings, Z. Zhang, and D. J. Maiman, “Transplanted

neurally modiﬁed bone marrowderived mesenchymal stem cells promote tissue pro-

tection and locomotor recovery in spinal cord injured rats,” Neurorehabilitation

and neural repair, vol. 25, no. 9, pp. 873–880, November/December 2011 Novem-

ber/December 2011.

[4] K. E. Thomas and L. D. F. Moon, “Will stem cell therapies be safe and eﬀective for

treating spinal cord injuries?” British medical bulletin, vol. 98, no. 1, pp. 127–142,

June 01 2011.

7 REFERENCES 8

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Gerhardt, G. Gholmieh, J. J. Granacki, R. Hampson, M. C. Hsaio, J. Lacoss, V. Z.

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[6] Z. F. M. Alain Destexhe and T. J. Sejnowski, Kinetic models of synaptic transmis-

sion., 2nd ed., ser. Methods in Neuronal Modeling (2nd ed.). Cambridge, MA:

MIT Press, 1998, pp. 1–26.

[7] H. Nakajima, M. Esashi, and T. Matsuo, “The pH response of organic gate ISFETs

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on, vol. 52, no. 12, pp. 2614–2619, 2005. Chapter 2

Silicon Neuromorphic

2.1 Introduction

Since the late 1980s when Carver Mead published his work on an analogue electronic cochlear [1], many researchers have increasingly turned their attention to the ﬁeld of bio-inspired electronic circuits. The term neuromorphic, introduced by Mead, refers to neural systems that have been created using electronic circuits. These circuits can be either on an analogue or digital platform. In the ﬁeld of neuromorphic VLSI, there are many recent studies that have designed integrated circuits to assist in hearing [2, 3], visual perception [4, 5, 6] and the sense of smell [7]. It can be said that neuromorphic engineering is one of the prominent applications in VLSI designs.

In this chapter, the deﬁnition and the structure of the neuron are discussed. It is important to study the biochemical properties of a neuron, such as the ion channels and the Nernst equation, to understand the behaviour of neurons. The trigger signal in the neuronal system, called the action potential, will also be described in detail and its

9 2.2. Neuron 10 mathematical model - the Hodgkin-Huxley, the integrated-ﬁre and the Morris-Lecar, examined. The Hodgkin and Huxley model, in particular, will be expanded to show the individual chemical current channels and their behaviour in simulation.

Another important part of this chapter is the synapse and its mathematical model. Bio- logical details of the chemical and electrical synapses will be described and, in particular, the Destexhe’s chemical synapse model which was used as the basis for electronic circuits implementation in this thesis. Furthermore, its simulation results from the mathematical simulator (MATLAB) will be presented. Finally, examples of the mathematical model-based neuron implementation using analogue electronics will be given.

2.2 Neuron

The brain, vertebrate spinal cords and peripheral nerves are constructed from the same vital parts called neurons. The function of a neuron is to couple neural signals from the brain to the targeted organ. Neural signals received at the dendrites of the neuron are re-transmitted along the axon via an electrochemical mechanism [8]. The main components of a typical neuron consist of the dendrites, a soma, a nucleus and the axons as shown in Fig.(2.1).

Another unique property of neurons is the ability to transmit electrical signals over long distances [9]. These signals travel through the cell membrane, which contains several types of ion channels that interact with the changes in the transmembrane potential. A transient pulse of charges across this transmembrane is called an action potential [10]. 2.2. Neuron 11

Dendrite Axon terminal

Node of Ranvier Soma

Myelin Sheath Schwann cell Nucleus Axon

Figure 2.1: The structure of a neuron

2.2.1 Ion channels and electrical properties of membranes

Ion channels are membrane proteins or an assembly of several proteins, which are directly responsible for the transport of inorganic ions. The most distinct characteristic of these channels is that over a million ions can cross a single ion channel per second. The function of an ion channel is to allow a particular inorganic ion, i.e. Na+,

K+, Ca2+ or Cl– to diﬀuse down their electrochemical gradients across the lipid bilayers.

The operation of the ion channels are controlled by the process of ion selectivity and the fluctuation between their open and closed states. The first property signifies that the ion channels will only permit certain inorganic ions to pass but not others. The second significant property indicates a gate mechanism of the ion channels, which opens briefly and then closes. There are many specific stimuli that actuate the ion channel’s gate. Some of the more well-known stimuli are:

Voltage-gated channels - ions channels that open owning to changes in the mem-

brane potential. 2.2. Neuron 12

Mechanical gated channels - ion channels that open under a mechanical stress.

Ligand-gated channels - ion channels that are stimulated by the binding of a

ligand. This ligand can be either an extracellular mediator (a neurotransmitter

or transmitted-gated channel), an intracellular mediator (ion-gated channel) or a

nucleotide (nucleotide-gated channels).

Most ion channels are sensitive to K+ ions. When these ion channels operate, their common function is to make the plasma membrane more permeable to K+ ions. This behaviour plays a vital role in the regulation of the membrane potential.

The potential diﬀerence between the inside and the outside of the membrane, termed the membrane potential, arises from the diﬀerence in electrical charges between the two sides of the membrane. In humans, the Na+ -K+ pump assists in the maintenance of the osmotic balance across the cell membrane by keeping a lower concentration of Na+ ions on the inside compared to the outside the cell.

2.2.2 Nernst Equation

The ﬂow of any ions through a membrane channel is driven by the electrochemical gradient. This gradient is inﬂuenced by both the voltage and the concentration gradient of ions across the cell membrane.

When the inﬂuence of these two factors are balanced, the electrochemical gradient for the ion is zero. The net ﬂow of the ion channel is also zero. The membrane potential

(voltage gradient) at this equilibrium is given by the Nernst equation in eq.(2.1). 2.3. Action potential and its model 13

RT Co Vmem = ln (2.1) zF Ci where V is the equilibrium membrane potential, Co is the ratio of the outside to mem Ci the inside ion concentration, R is the gas constant (8314.4mJ/K·mol), T is the absolute temperature in Kelvin, F is the Faraday’s constant 96, 485C/mol and z is the charge of the ion.

For an animal cell, the potential diﬀerence across the plasma membrane at equilibrium varies from -20mV to -200mV depending on the organism and the cell type. In hu- man beings, this potential at equilibrium, termed the resting potential is given by the

Goldman equation shown in eq.(2.2)

+ + − PK [K ]out + PNa[Na ]out + PCl[Cl ]out Vmem = 58mV ln + + − (2.2) PK [K ]in + PNa[Na ]in + PCl[Cl ]in

+ + where PK , PNa and PCl are the relative membrane permeability for K , Na and Cl− ions. [K+], [Na+] and [Cl−] are the concentration of the potassium, sodium and chloride ion. The subscriptions out and in refer to the outside and the inside of the membrane, respectively.

2.3 Action potential and its model

2.3.1 Generation of Action Potential

An action potential is triggered when the plasma membrane potential rises above its resting value. This event is termed as depolarisation. Depolarisation is the consequence 2.3. Action potential and its model 14 of a neurotransmitter-triggered response by the cell body. Owing to this depolarisation, the voltage-gated channel for the sodium ions opens and allows Na+ ions to move inside the cell. The amount of this migration is in accordance with its electrochemical gradient.

This open state of the Na+ channel remains until the membrane potential rises to

+50mV from the -70mV resting potential.

At a membrane potential of +50mV, a new equilibrium state is reached. However, the duration of this peak is short owing to an automatic inactivation of the Na+ channels.

This mechanism forces the sodium channels to shut rapidly even when the membrane is depolarised. With the sodium channels closed, the activation of the K+ channels begins to bring the membrane potential back to the resting level (-70mV). The opening of the K+ channels causes the K+ ions to dominate over the Na+ ions, which drives the membrane potential back towards the K+ ion equilibrium point. Fig.(2.2) shows a typical proﬁle of an action potential as described above.

2.3.2 Membrane potential model

The membrane potential has been modelled mathematically in various forms. The very

first model, the integrate and fire model, was published in 1907 by Lapicque [11]. No correlation between the membrane potential and the biophysical details was given in this model. The first qualitative membrane potential with correlation to the biophysical details was constructed from the experiment on a squid giant axon by Hodgkin and Huxley in 1952 [12]. The Hodgkin and Huxley model is based on three main ionic currents - sodium, potassium and leakage. More details on the Hodgkin and Huxley model will be described in a later section. Postsynaptic Vpre neuron

Synaptic vesicle Axon terminal Voltage-gated Ca++ channels

Neurotransmitter Synaptic receptors cleft

Dendrite spine Presynaptic Vpost neuron

2.3. Action potential and its model 15

Na+ channels become 3 +40 refractory, no more Na+ enters cell ) V 4 m (

l K+ continues to a i

t K+ channels leave cell and n

e open, K+ 2 causes membrane t

o begins to leave potential to return p cell to the resting potential e n a r

b Na+ channels

m open, Na+ e 5 M begins to enter K+ channels close, cell 1 Na+ channels rest

-70 time Threshold of 6 excitation Extra K+ outside Diffuses away

Figure 2.2: Typical Nerve Action Potential

The mechanism of the potassium and the sodium channels in the Hodgkin and Huxley

model are described by non-linear, time-dependent functions. Thus, the computational

algorithm or the electronic circuit implementation of the Hodgkin and Huxley model will

be complex. More recently, there has been several attempts to re-model the membrane

potential with a less complex mathematical function while still exhibiting the neuron

behaviour in the Hodgkin and Huxley model. In this section, three other membrane po-

tential models will be described: Integrate-and-Fire, FitzHughNagumo and MorrisLecar

model. 2.3. Action potential and its model 16

Integrate-and-Fire model: the simplest and the ﬁrst model, proposed in 1907 by

Lapicque [11]. This model is based on the current and voltage of a capacitor,

given by:

dV I(t) = C (2.3) m dt

where I(t) is the applied current, Cm is the membrane capacitance and V is the membrane potential. When the current is applied, the membrane potential rises

until the threshold voltage (Vth) is reached. After reaching Vth, the membrane potential will reset itself to the resting potential. From the hardware implemen-

tation aspect, the integrate and ﬁre model is the most compact among the neuron

models.

FitzHugh-Nagumo model: the model was published in 1961 by FitzHugh [13] and

later realised using electrical circuits by Nagumo et al. [14].

dV V 3 = V − − W + I dt 3 (2.4) dW = 0.08(V + 0.7 − 0.8W ) dt

where W is the recovery variable and I is the stimulus current.

The FitzHugh-Nagumo model can be classiﬁed as a reduced version of the Hodgkin

and Huxley model because the three current compartments (Na+,K+ and leakage)

have been reduced into a single variable equation.

Morris-Lecar model: another simpliﬁed model of Hodgkin and Huxley. There are

three current channels in this model: Ca2+,K+ and leakage. The equations for 2.3. Action potential and its model 17

this model are [15]:

dV C = −g M (V )(V − E ) − g W (V − E ) − g (V − E ) + I m dt Ca ss Ca K K L L dW W (V ) − W = ss dt τW (V ) 1 + tanh[ V −V1 ] M = V2 (2.5) ss 2 1 + tanh[ V −V3 ] W = V4 ss 2 V − V3 τW (V ) = τ0sech( ) 2V4

where W is the recovery parameter. gCa and gK are the conductance of calcium

and potassium channels, respectively. ECa and EK are the equilibrium potential

for calcium and potassium. Mss and Wss are the open state probability.

The Morris-Lecar model preserves the chemical channels as in the Hodgkin and

Huxley model. More importantly, the open state equation for each channel is less

complex and is more straightforward to implement than the Hodgkin and Huxley

model.

2.3.3 Hodgkin and Huxley model

The ﬁrst qualitative mathematical model of an action potential was published by Alan

L. Hodgkin and Andrew Huxley in 1952 [12, 16, 17, 18, 19]. From the voltage clamp experiment along the axon of a giant squid, Hodgkin and Huxley observed that the electrical current across the cell membrane depended on two factors:

1. The resistance of the cell membrane, and 2.3. Action potential and its model 18

2. The capacitance of the cell membrane

Extracellular

INa+ IK+ ILeak

Vm g g g Na+ K+ Leak Cm

ENa+ EK+ ELeak

Intracellular

Figure 2.3: Equivalent electrical circuit for the Hodgkin-Huxley model

Fig.(2.3) illustrates the components of the Hodgkin-Huxley model. The capacitance

Cm is the portrayal of a lipid bi-layer. The non-linear electrical conductances (gk+ and gNa+) control the voltage-gated ion channels. The leakage channel is represented by the linear conductance (gLeak). The equilibrium potential of each ion (ENa, EK and EL) represents their respective electrochemical gradient. The total current of the membrane consists of the capacitive current and the resistive current.

Current component of Hodgkin and Huxley model

With the voltage-dependent property of a capacitor, the capacitance current (Icap), the membrane capacitance (Cm) and membrane potential (vm) can be derived as:

dv I = C m (2.6) cap m dt 2.3. Action potential and its model 19

The resistive current is the voltage-dependent current (both membrane and equilibrium potential). The equilibrium potential of individual channels can be calculated from the Nernst equation (eq.(2.1)). From the circuit point of view, the ionic current in the membrane is directly proportional to the diﬀerence between the membrane and equilibrium potential, as shown in eq.(2.7).

Iion = gion(vm − Eion) (2.7)

The total membrane current (Im) for the model proposed by Hodgkin and Huxley can be given as:

Im = Icap + Iion (2.8)

dv I = C m + g (v − E ) (2.9) m m dt ion m ion

INa IK IL dv z }| { z }| { z }| { I = C m + g (t)(v − E ) + g (t)(v − E ) + g (v − E ) (2.10) m m dt Na m Na K m K L m L

where gNa(t), gK (t) and gL are the conductance of the sodium, potassium and leakage channel, respectively. ENa, EK and EL are the equilibrium potential of the sodium, potassium and leakage channel.

The experiment of Hodgkin and Huxley also concluded that gNa(t) and gK (t) are non- linear conductances whilst gL is linear. The time-dependence of the potassium and sodium channels was modelled by introducing a new variable that refers to the proba- 2.3. Action potential and its model 20 bility of the ionic gating process. This will be shown in the next section.

Note that the lowercase, vm, is the diﬀerence in the membrane potential, Vm(t), and its resting value, Vm(rest). Thus, the deﬁnition of vm is:

vm(t) = Vm(t) − Vm(rest) (2.11)

From eq.(2.11), vm mathematically diﬀers from Vm(t) by a constant. This means that the time-derivation of vm is equal to the corresponding derivatives of Vm(t).

Mathematical model for the potassium channel

The potassium conductance gK (t, vm) is the ﬁxed maximum conductance (when all

4 channels are open),g ¯K , multiplied by n : the fraction of the open channels (0 < n < 1). Thus,

4 gK (t, vm) =g ¯K n (t, vm) (2.12)

The variable n can be derived from the ﬁrst order kinetics:

dn(t, v ) m = α (v )(1 − n) − β (v )n (2.13) dt n m n m

From curve ﬁtting, the rate constants αn(vm) and βn(vm) are:

0.01(10 + v ) α = m (2.14) n 10+vm exp( 10 ) − 1 2.3. Action potential and its model 21 and

v β = 0.125 exp( m ) (2.15) n 80

−1 where vm is in mV and α, β are in (milli-second) . The potassium channel current is given by:

4 IK =g ¯K n (vm − EK ) (2.16)

Mathematical model for the sodium channel

The ionic current for the sodium channel has a similar model to the potassium channel, except that there are two control probability variables: m (activation) and h (inactivation), where:

3 gNa(t, vm) =g ¯Nam (t, vm)h(t, vm) (2.17)

Both parameters follow the ﬁrst-order diﬀerential equation similar to the variable n in the potassium channel as:

dm(t, v ) m = α (v )(1 − m) − β (v )m (2.18) dt m m m m and

dh(t, v ) m = α (v )(1 − h) − β (v )h (2.19) dt h m h m 2.3. Action potential and its model 22

The rate constants - αm, βm, αh and βh - were chosen from the curve ﬁtting as:

0.1(25 + v ) α = m (2.20) m 25+vm exp( 10 ) − 1

v β = 4 exp( m ) (2.21) m 18 and

v α = 0.07 exp( m ) (2.22) h 20

1 β = (2.23) h 30+vm exp( 10 ) + 1

−1 where vm is in mV and α, β are in (milli-second) . The sodium channel current is given by:

3 INa =g ¯Nam h(vm − ENa) (2.24)

Mathematical model for the leakage channel

As stated earlier, the conductance of the leakage channel is considered as a constant.

Thus, the leakage channel current is given by:

IL =g ¯L(vm − EL) (2.25) 2.3. Action potential and its model 23

The value of the variables mentioned in the sodium, potassium and leakage channel is shown in Table (2.1).

Table 2.1: Hodgkin and Huxley nerve axon model parameters Constant Name Units Values 2 Cm Membrane capacitance µF/cm 1 to 2.8 ENa Sodium equilibrium potential mV Vm(rest) + 115 EK Potassium equilibrium potential mV Vm(rest) − 12 EL Leakage equilibrium potential mV Vm(rest) − 10.613 2 g¯Na Sodium maximum conductance mS/cm 120 2 g¯K Potassium maximum conductance mS/cm 36 2 g¯L Leakage maximum conductance mS/cm 0.3

2.3.4 Single Compartment

In Table(2.1), some units of the Hodgkin and Huxley model parameter are per unit area.

Therefore, to synchronise the Hodgkin and Huxley model with the chemical synapse, a single neuron model, those units has to be transformed for a single compartment neuron.

Firstly, the exact area of a single neuron needs to be calculated. A single compartment of neurons is 10µm in diameter, 10µm in length (i.e. area of single neuron is π × 10−6cm2)

[20]. The transformed parameters from Table(2.1) are shown in Table(2.2).

Table 2.2: Transformed Hodgkin and Huxley axon model parameters Constant Name Units Values Cm Membrane capacitance pF 3.14159 to 8.79645 g¯Na Sodium maximum conductance nS 376.9911184 g¯K Potassium maximum conductance nS 113.0973355 g¯L Leakage maximum conductance nS 0.9424777961

Furthermore, the unit of vm in eq.(2.14), eq.(2.15), eq.(2.20), eq.(2.21), eq.(2.22) and eq.(2.23) is mV. To standardise this unit, these equations need to be transformed into

Volts. The transformations are shown in eq.(2.26) to eq.(2.31). 2.3. Action potential and its model 24

104(0.01 + v ) α = m (2.26) n 0.01+vm exp( 0.01 ) − 1

v β = 125 exp( m ) (2.27) n 0.08

The graph plots in MATLAB of eq.(2.26) and (2.27) are shown in Fig.(2.4).

(a) (b)

Figure 2.4: Rate constants (a) αn and (b) βn

The activation of the open state for the potassium channel (n) is a function of αn and

βn i.e. the ﬁrst order diﬀerential equation as shown in eq.(2.13). The plot of the n variable is shown in Fig.(2.5).

105(0.025 + v ) α = m (2.28) m 0.025+vm exp( 0.01 ) − 1

v β = 4×103 exp( m ) (2.29) m 0.018 2.3. Action potential and its model 25

Figure 2.5: Activation of potassium channel (n)

The graph plots in MATLAB of eq.(2.28) and (2.29) are shown in Fig.(2.6).

(a) (b)

Figure 2.6: Rate constants (a) αm and (b) βm

The activation variable of the sodium channel (m) is a function of αm and βm i.e. the ﬁrst order diﬀerential equation as shown in eq.(2.18). The plot of the variable m is shown in Fig.(2.7). 2.3. Action potential and its model 26

Figure 2.7: Activation of the sodium channel (m)

v α = 70 exp( m ) (2.30) h 0.02 103 β = (2.31) h 0.03+vm exp( 0.01 ) + 1

The graph plots in MATLAB of eq.(2.30) and (2.31) are shown in Fig.(2.8).

(a) (b)

Figure 2.8: Rate constant (a) αh and (b) βh 2.3. Action potential and its model 27

The inactivation variable for the sodium channel (h) is a function of αh and βh i.e. the ﬁrst order diﬀerential equation as shown in eq.(2.19). The plot of the variable h is shown in Fig.(2.9).

Figure 2.9: Inactivation of the sodium channel (h)

The action potential according to eq.(2.10) was also plotted. Its result is illustrated in

Fig.(2.10).

(a) (b)

Figure 2.10: (a) The action potential (vm) observed when applied with (b) the total membrane current (Im) 2.4. The Synapse 28

2.4 The Synapse

Communication between neurons is achieved via the transmission of action potentials.

This transmission is facilitated by synapses which acts as the medium. Synapses have a bulb-like structure and their function is to interconnect neurons with other targeted neurons. The synapse is the crucial part of a the neural communication system because it allows a neuron to instantly relay signals to one or more other neurons [21]. Synapses can be categorised into two types: electrical and chemical.

2.4.1 Electrical Synapse

For the electrical synapse shown in Fig.(2.11), the depolarisation of the presynaptic neuron is directly coupled to the postsynaptic neuron without any delay. The pre- and postsynaptic membrane of the electrical synapse are separated by a small gap junction

(3.5nm). The transmission of action potentials for this instance is simply a directly connected ionic current.

Owing to the ionic current movement at the gap junction, the direction of the transmission at the electrical synapses can be bidirectional. Other remarkable properties of electrical synapses are their speed and reliability. The delay due to this type of synaptic transmission is very small and can be negligible.

2.4.2 Chemical Synapse

In contrast with electrical synapses where the pre- and postsynaptic neurons are ad- hered to each other, the pre- and postsynaptic membrane of chemical synapses shown in Fig.(2.12) have a larger separation (20-40 nm), called a synaptic cleft. As a result, 2.4. The Synapse 29

Gap junction

Figure 2.11: Electrical synapse chemical synapses rely on the release of neurotransmitters from the presynaptic neuron.

The neurotransmitters are stored in the synaptic vesicles at the presynaptic terminal.

Once these neurotransmitters are emitted into the synaptic cleft, they will bind to a speciﬁc receptor at the postsynaptic neuron.

The detail of the chemical synaptic events from the pre- to the postsynaptic cell is summarised as [21]:

1. In the bouton of the postsynaptic neuron, the neurotransmitters are ﬁlled within

the vesicles. Most of these vesicles are incapacitated. When the presynaptic action

potential reaches the terminal arborisation of a bouton, the depolarisation induces

the voltage-gated calcium channel proteins to open and accept Ca2+ ions, which

causes the concentration of Ca2+ to increase from 100 nM to 100 µM. 2.4. The Synapse 30

Presynaptic neuron

Synaptic vesicle Axon terminal Voltage-gated Ca++ channels

Neurotransmitter Synaptic receptors cleft

Dendrite spine Postsynaptic neuron

Figure 2.12: Chemical synapse

2. An increase of intracellular [Ca2+] causes the vesicles to deliquesce and release

their neurotransmitters into the synaptic cleft. This process is called exocytosis.

3. The released neurotransmitter molecules bind to the receptors on the postsynaptic

cell membrane. This binding process leads to the opening and closing of ion

channels. The resulting ionic ﬂux causes the membrane conductance and the

membrane potential of the postsynaptic cell to ﬂuctuate.

Table (2.3), below, summarises the contrasting properties of electrical and chemical synapses.

2.4.3 Biological model for the chemical synapse

Model of neurotransmitter release

The relationship between the presynaptic action potential and the release of the neurotransmitter has been described in a mathematical model [22], which was simpliﬁed 2.4. The Synapse 31

Table 2.3: Summary Properties of Synapses Property Electrical Synapse Chemical Synapse Distance between pre- 3.5 nm 16-20 nm and postsynaptic cell membranes Cytoplasmic continuity between Yes No pre- and postsynaptic cells Ultrastructural components Gap-junction channels Presynaptic vesicles Agent of transmission Ion current Chemical transmitter Synaptic delay Negligible 0.3-5 ms, depending Direction of transmission Generally bidirectional Generally unidirectional from the calcium-induced release model [23]. Eq.(2.32) shows the relationship between the neurotransmitter concentration [T ] and the presynaptic voltage Vpre as:

Tmax [T ](Vpre) = (2.32) 1 + exp [−(Vpre − Vp)/Kp]

where Tmax is the maximal concentration of the neurotransmitter in the synaptic cleft,

Kp is the steepness and Vp is the half-activated function.

Kinetic model of the synapse

The relationship between the postsynaptic response and the neurotransmitter concentration was proposed by Destexhe et al. [20]. This response can be described in the

ﬁrst order kinetic regime as:

α R + T FGGGGGGGGGGB TR∗ (2.33) β where R and TR∗ are the unbound and bound state of the postsynaptic receptors, respectively. α and β are the forward and backward rate constant for the neurotransmitter binding. The fraction of bound receptor for this model is expressed using the law of 2.4. The Synapse 32 mass action [24], stated as:

dr = α[T ](1 − r) − βr (2.34) dt where [T ] is the concentration of the neurotransmitter and r is deﬁned as the fraction of the receptors in the open state. This neurotransmitter concentration is simpliﬁed and modelled as a pulse with a 1 ms duration and a 1 mM amplitude.

Model for the postsynaptic transmission

The mathematical model of the postsynaptic transmission has been simpliﬁed from the

Markov model of the postsynaptic current [25]. The postsynaptic membrane voltage

(Vpost) consists of the voltage-gated ion channels current (Iion) and the synaptic current

(Isyn), as shown in eq.(2.35) and eq.(2.36).

dV C post = −(I + I ) (2.35) m dt ion syn

Isyn = gsyn(t)(Vpost − Esyn) (2.36)

where Cm is the membrane capacitance, gsyn(t) is the time-dependent synaptic conductance and Esyn is the reversal potential of the channel.

There are four types of receptors that have been modelled [25]: AMP A, NMDA,

GABAA and GABAB. AMP A and NMDA are classiﬁed as the EPSP (excitatory postsynaptic potential) whilst GABAA and GABAB are considered as the IPSP (in- 2.4. The Synapse 33 hibitory postsynaptic potential). The postsynaptic current for each receptor is described as:

AMP A receptor:

dr AMP A = α [T ](1 − r ) − β r (2.37) dt AMP A AMP A AMP A AMP A

I = g r (V − E ) (2.38) AMP A AMP A AMP A AMP A where g is the maximal conductance (approximately 0.35 − 1.0 nS), r is the AMP A AMP A fraction of the receptor in the open state, V is the postsynaptic potential and EAMP A is the reversal potential (= 0mV). Obtaining the best ﬁt from the kinetic scheme,

6 αAMP A = 1.1 × 10 and βAMP A = 190. The plot of the variable rAMP A is shown in Fig.(2.13).

Figure 2.13: MATLAB simulation of rAMP A 2.4. The Synapse 34

NMDA receptor:

dr NMDA = α [T ](1 − r ) − β r (2.39) dt NMDA NMDA NMDA NMDA

1 B(V ) = 2+ (2.40) (−0.062V )[Mg ]o 1 + exp 3.57

I = g B(V )r (V − E ) (2.41) NMDA NMDA NMDA NMDA where g is the maximal conductance (approximately 0.01 − 0.6 nS), B(V ) is the NMDA 2+ magnesium block, [Mg ]o is the external magnesium concentration (1 to 2 mM in physiological conditions), rNMDA is the fraction of the receptors in the open state, V is the postsynaptic potential and ENMDA is the reversal potential (= 0mV). Obtaining 4 the best ﬁt from the kinetic scheme, αNMDA = 7.2 × 10 and βNMDA = 6.6. The plot of the variable rNMDA is shown in Fig.(2.14).

Figure 2.14: MATLAB simulation of rNMDA 2.4. The Synapse 35

GABAA receptor:

dr GABAA = α [T ](1 − r ) − β r (2.42) dt GABAA GABAA GABAA GABAA

IGABA = g rGABA (V − EGABA ) (2.43) A GABAA A A

where g is the maximal conductance (approximately 0.25-1.2 nS), rGABA is the GABAA A fraction of the receptors in the open state, V is the postsynaptic potential and E GABAA is the reversal potential (= -70 mV). Obtaining the best ﬁt from the kinetic scheme,

α = 5.3 × 105 and β =180. The plot of the variable r is shown in GABAA GABAA GABAA Fig.(2.15).

Figure 2.15: MATLAB simulation of rGABAA

GABAB receptor:

dr GABAB = K [T ](1 − r ) − K r (2.44) dt 1 GABAB 2 GABAB 2.4. The Synapse 36

du = K r − K u (2.45) dt 3 GABAB 4 u4 I = g (V − E ) (2.46) GABA GABA 4 GABA B B u + Kd B

where g is the maximal conductance (approximately 1 nS), rGABA is the fraction GABAB B of the activated receptors, V is the postsynaptic potential, u is the concentration of activated G-protein, and E is the reversal potential (= -95 mV). From curve GABAB 4 4 −1 −1 ﬁtting, the following values were obtained: Kd = 100µM , K1 = 9×10 M s ,

−1 −1 −1 K2 = 1.2 s , K3 = 180 s , K4 = 34 s and n = 4 binding site. The plot of the variable r is shown in Fig.(2.16). GABAA

Figure 2.16: MATLAB simulation of rGABAB

2.4.4 Postsynaptic simulation

The postsynaptic potential of the AMP A, NMDA, GABAA and GABAB receptors are simulated according to eq.(2.35). The terms Iion and Isyn in this equation refer to the Hodgkin and Huxley ionic current and the synaptic receptor current, respectively.

The resting potential in this case is assumed to be 100mV. The Hodgkin and Huxley 2.4. The Synapse 37 parameters for this resting potential are:

4 10 (0.11 + Vm) 0.1 + Vm dn αn = βn = 125 exp( ) = αn(1 − n) − βnn 0.11+Vm 0.08 dt exp( 0.01 ) − 1 5 10 (0.125 + Vm) 3 0.1 + Vm dm αm = βm = 4×10 exp( ) = αm(1 − m) − βmm 0.125+Vm 0.018 dt exp( 0.01 ) − 1 3 0.1 + Vm 10 dh αh = 70 exp( ) βh = = αh(1 − h) − βhh 0.02 0.13+Vm dt exp( 0.01 ) + 1

The potassium, sodium and leakage currents for a 100mV resting potential are:

4 3 IK =g ¯K n (Vm − 0.112) INa =g ¯Nam h(Vm + 0.015) IL =g ¯L(Vm − 0.089387)

The simulation of the postsynaptic transmission for the AMP A, NMDA, GABAA and

GABAB receptors are shown below.

AMP A postsynaptic simulation

The postsynaptic simulation of the AMP A receptor was based on:

dVm Cm = −INa − IK − IL − IAMP A dt (2.47) IAMP A =g ¯AMP ArAMP A(Vm − EAMP A)

where the reversal potential for AMP A (EAMP A) with a 100mV resting potential is

170mV and the maximal conductance for AMP A (¯gAMP A) is 0.1nS. The MATLAB 2.4. The Synapse 38 simulation of the AMP A receptor is shown in Fig.(2.17) and (2.18).

(a) (b)

Figure 2.17: (a) Single spike of AMP A neurotransmitter and (b) its postsynaptic response

(a) (b)

Figure 2.18: (a) Four spikes of AMP A neurotransmitter and (b) its postsynaptic response

NMDA postsynaptic simulation

The postsynaptic simulation of the NMDA receptor was based on:

dV C m = −I − I − I − I m dt Na K L NMDA

INMDA =g ¯NMDAB(Vm)rNMDA(Vm − ENMDA) (2.48) 1 B(Vm) = 2+ (−0.062Vm)[Mg ]o 1 + exp 3.57 2.4. The Synapse 39

where the reversal potential for NMDA (ENMDA) with a 100mV resting potential is

170mV and the maximal conductance for NMDA (¯gNMDA) is 0.1nS. The MATLAB simulation of the NMDA receptor is shown in Fig.(2.19) and (2.20).

(a) (b)

Figure 2.19: (a) Single spike of NMDA neurotransmitter and (b) its postsynaptic response

(a) (b)

Figure 2.20: (a) Four spikes of NMDA neurotransmitter and (b) its postsynaptic response

GABAA postsynaptic simulation

The postsynaptic simulation for the GABAA receptor was based on: 2.4. The Synapse 40

dVm Cm = −INa − IK − IL − IGABA dt A (2.49)

IGABAA =g ¯GABAA rGABAA (Vm − EGABAA )

where the reversal potential for GABAA (EGABAA ) with a 100mV resting potential is

90mV and the maximal conductance for GABAA (¯gGABAA ) is 0.1nS. The MATLAB simulation of the GABAA receptor is shown in Fig.(2.21) and (2.22).

(a) (b)

Figure 2.21: (a) A single spike of GABAA neurotransmitter and (b) its postsynaptic response

(a) (b)

Figure 2.22: (a) Four spikes of GABAA neurotransmitter and (b) its postsynaptic response 2.4. The Synapse 41

GABAB postsynaptic simulation

The postsynaptic simulation for the GABAB receptor was based on:

dV C m = −I − I − I − I m dt Na K L GABAA u4 I = g (V − E ) GABA GABA 4 GABA B B u + Kd B (2.50) dr GABAB = K [T ](1 − r ) − K r dt 1 GABAB 2 GABAB du = K r − K u dt 3 GABAB 4

where the reversal potential for GABAB (EGABAB ) with a 100mV resting potential is

75mV and the maximal conductance for GABAB (¯gGABAA ) is 0.1nS. The MATLAB simulation of the GABAB receptor is shown in Fig.(2.23).

(a) (b)

Figure 2.23: (a) Ten spikes of GABAB receptor and (b) its postsynaptic response

The mathematical model of the chemical synapse receptors, addressed in this section

(i.e. AMPA in eq.(2.47), NMDA in eq.(2.48), GABAA in eq.(2.49) and GABAB in eq.(2.50), will be implemented using analogue current-mode CMOS circuits operated in weak inversion region. These electronics circuit implementations will be described in

Chapter 5. 2.5. Silicon neuromorphic circuits 42

2.5 Silicon neuromorphic circuits

Since the idea of implementing neuromorphic systems on silicon was initiated by Mead and his collaborators in the late 1980s and the early 1990s [1, 26], bio-inspired systems on the CMOS platform has captured many researchers’ imagination.

In this section, reviews of the silicon neurons (mostly based on the Hodgkin-Huxley model) and the synapse models will be presented.

2.5.1 Silicon Neurons

The Hodgkin and Huxley model is a conductance-based neuron model. This model was

firstly implemented on silicon by Mahowald et al. in 1991 [27]. Mahowald’s silicon neuron consists of a CMOS operational transconductance amplifier (OTA), a differential pair and a current mirror operated in the weak inversion region. These circuit components were able to duplicate the non-linear, time-dependent functions in the Hodgkin and Huxley model.

According to the realisation of a current-mode integrator which performed mathematically the Bernoulli’s equation, Drakakis et al. [28] and Toumazou et al. [29] demonstrated that this Bernoulli cell can duplicate the activation variable n (in eq.(2.13)) of the potassium ion channel in the Hodgkin and Huxley model, as shown in Fig.(2.24)

Later in 2007, Lazaridis et al. [30] produced an implementation of the rate constant

αn, in eq.(2.14), in a subthreshold CMOS circuit. This implementation consisted of a CMOS operational transconductance ampliﬁer, the E-cell and a translinear loop circuit. 2.5. Silicon neuromorphic circuits 43

I I K+ channel Na L L g

IK(Iout1) IDn I0 Iout Cmem

channel EL + Na

IDn I En I0

Figure 2.24: Hodgkin and Huxley implementation on CMOS of Toumazou et al. [29]

Lazaridis ’s work illustrated the ability to fully implement the Hodgkin and Huxley neuron model on the CMOS platform.

Owing to the complexity of the mathematics required to calculate the variables in the

Hodgkin and Huxley model that is reﬂected by the circuit realisation, Farquhar et al. proposed a contrasting idea to implement the Hodgkin and Huxley model with less components than previous designs [31]. In Farquhar’s work, the similarity between the non-linear characteristics of the MOSFET current and those of the variables in the

Hodgkin and Huxley model were compared. As a result, Farquhar succeeded in creating a CMOS version of the Hodgkin and Huxley neuron with just six MOSFETs and three capacitors. 2.5. Silicon neuromorphic circuits 44

2.5.2 Silicon Synapses

Ludovic et al. [32] created a Bi-CMOS circuit to duplicate the fraction of the receptors in the open state (r), in eq.(2.34). This was achieved by transforming eq.(2.34) into an exponential decay function, where a resistive-capacitive circuit was employed together with a bipolar junction transistor (BJT) to formulate this function. This work can be considered as the ﬁrst CMOS synapse based on the model of Destexhe [22].

In 2006, Lazaridis et al. applied the Bernoulli cell [33] to duplicate r with a weakly inverted CMOS circuit. The Bernoulli cell implementation of r required four NMOS transistors and a capacitor. This circuit conﬁguration, shown in Fig.(2.25) is equivalent to a current-mode low pass ﬁlter. This synapse circuit based on the model of Destexhe

[22] is the ﬁrst implementation which employs the CMOS current mode log domain

ﬁlter in subthreshold CMOS technology.

A synapse implemented with only a few transistors and capacitors was reported by Gor- don et al. [34]. This idea uses a ﬂoating gate MOSFET where its gate was controlled with biased capacitors and a CMOS inverter. Gordon’s synapse transistor circuit is shown in Fig.(2.26).

This floating gate MOSFET with a CMOS inverter gave a waveform which fits the biological synapse model of Rall [35]. Furthermore when the bias potential of this circuit was properly tuned, its output waveform would match the postsynaptic potential of the excitatory and inhibitory synapse recorded from the neurons experiment of Wall et al. [36]. However, this floating gate CMOS circuit is not suitable for implantable applications. This is because the high current and high voltage properties of this circuit Iout X

I D1 I D2 V V in M1 M2  in 2 2

I x1

Ib Iout

VS i DS iDS Vg Vb

VB V G I DS (Vs Vg ) (n 1)I DS (Vs Vb ) nUT nUT

VD Vin Vin

1 + V i b n 1 - DS + n - 1 n

I x2

Iout

V V in  in 2 2

I I K+ channel Na L L g l

IK(Iout1) e I I0 Iout

n n

C n mem a h c

EL + a N

In  I n I0

2.5. Silicon neuromorphic circuits 45

I[T ] I0 Iout

I[T ] I  I0

Figure 2.25: r implementation with a Bernoulli cell by Lazaridis et al. [33]

might be harmful to organisms.

Another interesting work on a biomimetic synapse is the use of a MOSFET-based mem-

ory device to match the function of a synapse. Yu et al. [37] reported the potential use

of a metal oxide resistive switching memory for this application. This device is made of

Titanium Nitride (TiN), Hafnium Oxide (HfOx), Aluminium Oxide (AlOx) and Plat- inum (Pt), as the base materials. This non-volatile memory device is a simple capacitor

network which performs an integration to duplicate the function of synapses. The ben-

eﬁt from the synapse implementation on this approach is that it requires comparatively

smaller chip area than the conventional electronic circuit.

From the implementations of the synapse shown earlier in this section, there are no Iout X

I D1 I D2 V V in M1 M2  in 2 2

I x1

Ib Iout

VS i DS iDS Vg Vb

VB V G I DS (Vs Vg ) (n 1)I DS (Vs Vb )

nUT nUT

VD Vin Vin

1 + V i b n 1 - DS + n - 1 n

I x2

Iout

V V in  in 2 2

I I K+ channel Na L L g l

IK(Iout1) e I I0 Iout

n n

C n mem a h c

EL + a N

In  I n I0

I[T ] I0 Iout

I[T ] I  I0

2.6. Summary 46

ECa

Vp Vtun

Vin

Vout Vn

Figure 2.26: Gordon’s synapse circuit

implementation that has been formulated for a speciﬁc receptor type or with actual

neurotransmitter sensing. The integration between an electronic circuit which performs

the chemical synapse function, and a chemical sensor, which has the capability to de-

tect neurotransmitters, will lead to a complete OR a fully-functional bionics chemical

synapse. An integrated implementation of the chemical synapse will be presented later

in Chapter 5.

2.6 Summary

In this chapter, the principle and the biological aspects of neurons were introduced. The

action potential, the signal used for neuron communication, was described. Further-

more, various models of the membrane potential were mathematically explained, such 2.6. Summary 47 as the Hodgkin-Huxley model, the integrate-ﬁre model, the FitzHugh-Nagumo model and the Morris-Lecar model. The Hodgkin-Huxley model was examined in greater detail, especially the function of the current channels (Na and K) which was also simulated in MATLAB.

The other main content of this chapter is the function of synapses. The chemical synapse model by Destexhe was introduced and its simulation results on MATLAB were shown. Moreover, from the aspect of the bio-inspired circuits, examples of the silicon implementation of neurons and synapses were reviewed. References

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2011. Chapter 3

Low-gain OTA design

3.1 Introduction

At present, a CMOS analogue circuit operated in weak inversion plays an important role in biomedical applications. Beneﬁts from this operation range are not only low power consumption which is the key for implantable systems but also direct analogue computation which required for real time processors. One important element in analogue- computational systems are Operational Transconductance Ampliﬁers (OTAs).

A transconductance ampliﬁer is the key element in the silicon implementation of biological or bio-inspired systems, for example the realisation of the Hodgkin and Huxley neuron model [1, 2, 3] and the pancreatic cell in [4]. In this thesis, the bio-inspired circuit of a chemical synapse is implemented according to the Destexhe’s model [5] where the conductance of each synaptic compartment is 0.1nS. However an operational transconductance ampliﬁer (OTA) operating in the subthreshold region with a nano-

Ampere range bias current, produces the lowest transconductance gain in the order of

53 3.2. Analysis of diﬀerential pair as an OTA 54 nano-Siemens.

To achieve the required level of transconductance gain (0.1nS) while maintaining the input bias current in the range of nano-Ampere, the OTA design of Sarpeshkar et al.

[6] will be modiﬁed by combining it with the OTA linearisation technique of DeWeerth et al. [7]. The OTA of Sarpeshkar produces a transconductance gain of 2.29nS for a

4nA input bias current, however the modiﬁed OTA presented in this thesis can achieve a transconductance gain of 0.1nS with the same input bias current.

3.2 Analysis of diﬀerential pair as an OTA

This section presents the circuit analysis of a normal diﬀerential pair OTA. This diﬀer- ential pair is shown in Fig.(3.1).

Iout X

I D1 I D2 V V in M1 M2 in 2 2

Figure 3.1: A differential pair transconductance amplifier 3.2. Analysis of differential pair as an OTA 55

At node X, a KCL analysis gives:

ID1 = IOUT + ID2 (3.1)

When this diﬀerential pair is biased in the weak inversion region and all the transistors are perfectly matched, the output current (IOUT ) is equal to:

IOUT = ID1 − ID2 (3.2) W V in −V in = I exp( ) − exp( ) 0 L 2 2

The relationship between Ib, ID1 and ID2 is:

Ib = ID1 + ID2 (3.3) W V in −V in = I exp( ) + exp( ) 0 L 2 2

The term ( IOUT ) is a division of eq.(3.2) by (3.3). Thus: Ib

W Vin −Vin I I0 exp( ) − exp( ) OUT = L 2nUT 2nUT W Vin −Vin Ib I0 exp( ) + exp( ) L 2nUT 2nUT exp( Vin ) − exp( −Vin ) = 2nUT 2nUT (3.4) exp( Vin ) + exp( −Vin ) 2nUT 2nUT V = tanh( in ) 2nUT

A Taylor’s series of tanh(x) is: 3.3. Analysis with signal ﬂow graph technique 56

x3 2x5 17x7 tanh(x) = x − + − + ...+ (3.5) 3 15 315

Therefore, the Taylor’s series of tanh( Vin ) in eq.(3.4) is: 2nUT

V V V ! V ( in )3 2( in )5 17( in )7 I = I in − 2nUT + 2nUT − 2nUT + ... + OUT b 2nU 3 15 315 T (3.6) Ib ≈ Vin 2nUT where Ib is the transconductance gain of the diﬀerential pair. 2nUT

3.3 Analysis with signal ﬂow graph technique

Alternatively, the analysis of an OTA can be represented by a block diagram of the half circuit and a signal ﬂow graph as proposed by R. Sarpeshkar et al. [6]. First of all, let us consider the equation for the drain current of a subthreshold MOSFET:

W VGS (n − 1)VBS iDS = I0 exp( ) exp( ) (3.7) L nUT nUT

The transconductance of the gate, bulk and source are the derivatives of eq.(3.7):

∂iDS ids ID ggate = = = ∂vG vg nUT ∂iDS ids n − 1 gbulk = = = ( )ID (3.8) ∂vB vb nUT ∂iDS ids ID gsource = = = ∂vS vs UT 3.3. Analysis with signal ﬂow graph technique 57

Sarpeshkar also recommended that all small signal parameters should be dimensionless

(i = id/ID or v = vd/UT ), therefore id = gdvd = IDvd/nUT which simply is i = v/n. In this case, n is considered as a dimensionless transconductance. From eq.(3.8), the dimensionless transconductance of the gate, the bulk and the source are 1/n,(n − 1)/n and 1, respectively. The small signal equivalent circuit and the signal ﬂow graph of a

MOSFET are shown in Fig.(3.2(b)) and (3.2(c)), respectively.

V S V i s iDS V DS V g b 1 + V i I (V V ) b n 1 - DS DS s g (n 1)I DS (Vs Vb ) + VB V n G nUT nUT - 1 n

V V D Vd g (a) (b) (c)

Figure 3.2: (a) A transistor with corresponding voltages and currents. (b) The small signal equivalent circuit for the bulk transistor. (c) The signal ﬂow graph of dimensionless model for the bulk transistor.

An OTA with double source degeneration transistors is shown in Fig.(3.3). The equivalent half circuit and signal ﬂow graph are illustrated in Fig.(3.4(a)) and (3.4(b)), respectively.

The dimensionless transconductance of the gate (1/n2) is attenuated by a feedback factor of the drain (n1) and the source (n3n4). The overall dimensionless transconductance for this circuit is given by:

1/n g = 2 (3.9) 1 + n1 + (n3n4) 3.3. Analysis with signal ﬂow graph technique 58

Iout

V V in in 2 2

Figure 3.3: Diﬀerential pair as an OTA with double source degeneration

1 n4

1 V in 2 2 + i + n1 DS 3 - 1

n2 4

(a) (b)

Figure 3.4: (a) The half equivalent circuit of OTA in Fig.(3.3). (b) The signal ﬂow graph of Fig.(3.4(a))

Let n1 = np and n2 = n3 = n4 = nn. Thus, eq.(3.9) becomes: 3.3. Analysis with signal ﬂow graph technique 59

1 g = 2 (3.10) nn(nn + np + 1) g is the dimensionless parameter with Ib as the multiplication factor to obtain the 2UT actual transconductance. Therefore, the output current from this double source degeneration is:

Ib Iout = 2 Vin (3.11) 2nn(nn + np + 1)UT

Ib where the tranconductance gain (gm) in this case is 2 . With Ib = 5nA, 2nn(nn+np+1)UT nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, the theoretical gm for this circuit is 18.76nS. This calculation is conﬁrmed by the Cadence simulation result (19.4nS), shown in Fig.(3.5)

-1.4

-1.6 A.) -9

-1.8

-2.0 Output current (x10

-2.2

0 10 20 30 40 50 Input differential voltage (x10-3 V.)

Figure 3.5: Simulation result for transconductance ampliﬁer in Fig.(3.3) 3.4. Analysis of a bulk driven OTA with source degeneration and bump linearisation60

3.4 Analysis of a bulk driven OTA with source degenera-

tion and bump linearisation

To further reduce the transconductance gain and increase the linear range of the OTA,

R. Sarpeshkar et al. [6] demonstrated a circuit of the diﬀerential pair OTA with a bulk input and bump linearisation [8]. This OTA circuit with two extra diode-connected

PMOS transistors is shown in Fig.(3.6).

Iout

V V in in 2 2

B1 MN1 MN2

Figure 3.6: Bulk diﬀerential pair as an OTA with double source degeneration

First, let’s consider the circuit without considering of the bump linearisation [8] transistors, shown in the shaded area, the half circuit of this OTA and its signal ﬂow graph are illustrated in Fig.(3.7(a)) and (3.7(b)), respectively. The dimensionless conductance

(g) for this case is given by: Iout X

I D1 I D2 V V in M1 M2  in 2 2

I x1

Ib Iout

VS i DS iDS Vg Vb

VB V G I DS (Vs Vg ) (n 1)I DS (Vs Vb ) nUT nUT

VD Vin Vin

1 + V i b n 1 - DS + n - 1 n

I x2

I out P5

n 5.6 5.6 V V Vs P5 in  in 5.6 5.6 2 2 P7 P3 1 I x1 P8 nP3 + i n 1 - DS P1 + n V Vin P1 in P1 - 100 100 I 2 2 0.35 0.35 out 1 P5 nN 2 P6 nP1

Vg 100 100 N2 0.35 0.35 200 200 P3 P4 0.35 0.35 Ib N13 N14

100 100 0.35 0.35

P1 P2 200 200 Vin 0.35 0.35 Vin nN 7 N11 N12 N13 n 67.2 N 9 N3 5.6 Vs V in 200 200 N11 1 N5 2 5.6 5.6 67.2 0.35 0.35 N6 + 5.6 5.6 5.6 5.6 5.6 N9 N10 + nN13 iDS N1 N2 N4 5.6 5.6 1 n N9 3 - 200 200 n 4 1 0.35 0.35

Vs N7 n V N11 N7 N8 in 2 1 2 Vg + I x2

+ n iDS 3 1 - 1 n2 4 5.6 5.6 Vg 5.6 5.6

I x1

100 100 I 0.35 0.35 out

100 100 0.35 0.35 200 200 0.35 0.35

100 100 0.35 0.35

1 Ib 200 200 Vin 0.35 0.35 Vin

3.4. Analysis of a bulk driven OTA with source degeneration and bump linearisation61 67.2 5.6 Iout 2 200 200 5.6 5.6 67.2 0.35 0.35 1 V s 5.6 5.6 5.6 n1 5.6 5.6 1 5.6 5.6 2 n2 + i V Vb DS 200 200 in n3 1 - 3 + 0.35 0.35 n3 2 V in 3 2 - Vin Vin 1  n n 4 2 3 2 I x2

4 Vg

B2 (a) (b) 4 B1 Figure 3.7: (a) The half equivalent circuit of OTA in Fig.(3.6). (b) The signal ﬂow graph of Fig.(3.7(a)) MN1 MN2 I x1 ( n3−1 ) g = n3 (3.12) 1 + n4 + n n n3 1 2

The subthreshold parameters are: n1 = n2 = n4 = nn and n3 = np. The dimensionless Iout transconductance in eq.(3.12) is rearranged as:

1 (1 − n ) g = p (3.13) 1 + nn + n2 np n n3 Additionally, the extra two transistors (shaded1 in Fig.(3.6)) can be used to divide the n4 current from the diﬀerential pair and therefore reduce the transconductance gain. This Vs V technique is called theinbump linearisation 2[8]. The ratio between bump transistors (B1 1 and B2) and transistors2 (MN1 and MN2), ( SB1,2 ) or w, determines the transconduc- SMN1,2 + tance gain according to eq.(3.14). + n iDS 3 1 V V - in in 1

n2 4

I x2 3.5. Analysis of double diﬀerential pair OTA 62

sinh x I = (3.14) out β + cosh x where x = gVinIb and β = (1 + w ). In this thesis, w is set to 12. Thus, eq.(3.14) can be 2UT 2 expanded into a Taylor series as shown in eq.(3.15).

sinh x I = out 7 + cosh x x 5x3 13x5 79x7 3407x9 = + − − + + ··· (3.15) 8 384 30720 2064384 1486356480 x ≈ 8

From the approximation in eq.(3.15), the output current of this OTA will be:

(1 − 1 )I np b Iout = Vin (3.16) 16U (1 + nn + n2 ) T np n

(1− 1 )I np b where the transconductance gain (gm) is 16U (1+ nn +n2 ) . With Ib = 5nA, nn = 1.3, np T np n = 1.28 and UT = 25.82mV @ 300K, the theoretical tranconductance gain is 0.724 nS while the Cadence simulated result was 0.792 nS, shown in Fig.(3.8).

3.5 Analysis of double diﬀerential pair OTA

The intention for using the transconductance amplifier in this thesis is to generate the low synaptic conductance (0.1nS) [5]. This is the main reason to make further modifications to the OTA, shown in Fig.(3.6), to acquire an even lower transconductance gain. The technique for this modification was proposed by DeWeerth et al. [7] and

Simoni et al. [1], as shown in Fig.(3.9). 3.5. Analysis of double diﬀerential pair OTA 63

-50

-100 A.)

-12 -150

-200

-250 Output current (x10

-300

0.0 0.1 0.2 0.3 0.4 Input differential voltage (V.)

Figure 3.8: Simulation result between output current and diﬀerential input voltage of the OTA in Fig.(3.6)

This technique employs two diﬀerential pair OTAs. The outer diﬀerential pair (M5 and

M6) senses the change in the drain potential of the inner diﬀerential pair (M1 and M2).

The overall transconductance gain can be analysed by considering the inner diﬀerential pair ﬁrst.

With respect to the half circuit (only M1 is shown in Fig.(3.10)) of the inner diﬀerential pair, the transconductance gain of the transistor M1 (gm1) is given by:

δi g = d1 m1 ∂v in (3.17) IA = 2 nUT

vin = V1 − V2 and IA is the bias current. The change in the M1 drain current (∂id1) alters the source potential of M3 (V5). The relationship between id1 and v5 is according

δid ID to the source tranconductance gs = = : δvs UT 3.5. Analysis of double diﬀerential pair OTA 64

M7 M8

Iout

Id5 Id6 M3 M4

V5 V6 M5 M6 Id1 Id2

V1 M1 M2 V2

Figure 3.9: Variable linear range OTA of S.P. DeWeerth et al.

δid δv5 = gs5 IA 2 δvin = nUT (3.18) IA 2 UT δv = in n

gs5 is the source transconductance of M5. δv5 is conveyed as the input of the outer 3.5. Analysis of double diﬀerential pair OTA 65

Id1

V1 M1

IA 2

Figure 3.10: Half circuit of the inner differential pair of Fig.(3.9) differential pair. For the outer differential pair OTA, the relationship between the output current (Iout) and V5 − V6 is given by:

IB Iout = (V5 − V6) (3.19) 2nUT

The overall transconductance gain of the circuit in Fig.(3.9) is calculated by substituting eq.(3.18) into (3.19) and letting δv5 = V5 − V6 and δvin = V1 − V2.

IB 1 1 Iout = · · ·(V1 − V2) 2 nUT n | {z } |{z} g g2 1 (3.20) IB = 2 (V1 − V2) 2n UT | {z } gm

From eq.(3.20), it can be concluded that the overall transconductance gain is the product of the dimensionless transconductance g1 and g2 of the diﬀerential pairs M5,6 and

M1,2, respectively. 3.5. Analysis of double diﬀerential pair OTA 66

Using the similar topology as the circuit shown in Fig.(3.9), the equivalent transconduc-

tance gain of the OTA in Fig.(3.11) can be calculated in the same regime. On the LHS

of Fig.(3.11) is the bulk driven OTA, analysed in the previous section, and on the RHS

(shaded) is the NMOS diﬀerential pair OTA with double source degeneration transis-

tors. The other two transistors on top of the RHS diﬀerential pair are diode-connected

MOSFETs, which act as the load. The half circuit and signal ﬂow graph diagram of

the shaded OTA are shown in Fig.(3.12(a)) and (3.12(b)), respectively.

Iout X

I D1 I D2 V V in M1 M2  in 2 2

I x1

Ib Iout

VS i DS iDS Vg Vb

VB V G I DS (Vs Vg ) (n 1)I DS (Vs Vb ) nUT nUT

VD Vin Vin

1 + V i b n 1 - DS + n - 1 n

I x2

I Figure 3.11: The double diﬀerential pair OTA out P5 n 5.6 5.6 V V Vs P5 in  in 5.6 5.6 2 2 P7 P3 1 I x1 P8 nP3 + i n 1 - DS P1 + n V Vin P1 in P1 - 100 100 I 2 2 0.35 0.35 out 1 P5 nN 2 P6 nP1

Vg 100 100 N2 0.35 0.35 200 200 P3 P4 0.35 0.35 Ib N13 N14

100 100 0.35 0.35

Vs N7 n V N11 N7 N8 in 2 1 2 Vg + I x2

+ n iDS 3 1 - 1 n2 4 5.6 5.6 Vg 5.6 5.6

I x1

n1 1 100 100 n I + 2 0.35 0.35 out Vb iDS n3 1 - + n3 - 1 100 100 n4 n3 0.35 0.35 200 200 0.35 0.35

100 100 0.35 0.35

1 Ib 200 200 Vin 0.35 0.35 Vin Vs

n1 67.2 5.6 Iout 2 1 200 200 n 5.6 + 2 5.6 67.2 0.35 0.35 5.6 5.6 5.6 Vb iDS 5.6 5.6 n3 1 - 5.6 5.6 + V n3 200 200 in 3 2 - 0.35 0.35 V V  in in 1 2 2 n4 I x2 n3

B2 4 Vg B1 MN1 MN2 I x1

Iout

n3 1 n4

Vs V in 2 2 1 +

+ n iDS 3 1 V V - in in 1

n2 4

I x2 3.5. Analysis of double diﬀerential pair OTA 67

n4 1 Vs

V in 2 1 2 + i + n1 DS 3 - 1

n2 4

(a) (b)

Figure 3.12: (a) The half equivalent circuit of OTA in Fig.(3.11). (b) The signal ﬂow graph of Fig.(3.12(a))

From the signal ﬂow graph in Fig.(3.12(b)), the dimensionless transconductance of the

OTA on the RHS is:

1 n2 gRHS = (3.21) 1 + n1 + n3n4

where n1, n2, n3 and n4 are the weak inversion slope of the transistor 1, 2, 3 and 4, respectively. In this case all transistors are NMOS, eq.(3.21) can be rewritten as:

1 gRHS = 2 (3.22) nn(nn + nn + 1)

For the complete OTA circuit in Fig.(3.11), the output current is: 3.5. Analysis of double diﬀerential pair OTA 68

gLHS gRHS Ix1 Iout = (Vin+ − Vin−) 2UT (1 − 1 )I (3.23) np x1 = (Vin+ − Vin−) n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

1 (1− )Ix1 np where nn 2 2 is the transconductance gain. With Ix1 = 5nA, nn nn(1+ +n )(n +nn+1)16U np n n T = 1.3, np = 1.28 and UT = 25.82mV @ 300K, the theoretical gm for this circuit is 1.3968×10−10 S. This is conﬁrmed by the simulation result, shown in Fig.(3.13), where the obtained tranconductance gain was 1.2615×10−10 S. It should be noted that the tranconductance gain is independent of Ix2 when the RHS diﬀerential pair is operated in the subthreshold region [7].

20 A.) -12 10

0 Output current (x10

-10

0.0 0.1 0.2 0.3 0.4 Input differential voltage (V.)

Figure 3.13: Simulation result between output current and diﬀerential input voltage of the OTA in Fig.(3.11)

The important parameters of each transconductance design are summarised in Table

(3.1). 3.6. Summary 69

Table 3.1: Important parameters of each OTAs design

OTA topology Linear range Transistor count Theoretical dimensionless gm A diﬀerential pair in Fig.(3.1) 60mV 4 0.769 A diﬀerential pair with two source degenera- 260mV 8 0.193 tion in Fig.(3.3) DeWeerth et al. [7] (Fig.(3.9)) 3V 8 0.591 Sarpeshkar et al. [6] (Fig.(3.6)) 3.4V 14 0.00747 The OTA proposed in this work (Fig.(3.11)) 2.5V 22 0.00143

3.6 Summary

In this chapter, the analysis and the design of a low gain transconductance ampliﬁer have been presented. A traditional diﬀerential pair OTA operated in the weak inversion region was described and analysed. The transconductance gain, a hyperbolic function is transformed into a Taylor series.

The circuit analysis technique called signal ﬂow graph [6] was explained with a circuit analysis example. This technique is useful for analysing complex OTA topologies. A diﬀerential pair with a MOSFET body input was introduced to decrease the transconductance gain and increase the linear range. Furthermore, the analysis of the source degeneration and the bump linearisation techniques was presented.

At the end of this chapter, the ﬁnal double diﬀerential pair OTA design was analysed.

This OTA combined all the previously mentioned design techniques and was able to decrease the transconductance gain to the range of sub-nano Siemens (0.1nS for a 3.6nA bias current). References

[1] M. F. Simoni, G. S. Cymbalyuk, M. E. Sorensen, R. L. Calabrese, and S. P. De-

Weerth, “A multiconductance silicon neuron with biologically matched dynamics,”

Biomedical Engineering, IEEE Transactions on, vol. 51, no. 2, pp. 342–354, Feb.

2004.

[2] E. M. Drakakis, A. J. Payne, and C. Toumazou, “Log-domain state-space: a sys-

tematic transistor-level approach for log-domain ﬁltering,” Circuits and Systems II:

Analog and Digital Signal Processing, IEEE Transactions on, vol. 46, no. 3, pp.

290–305, 1999.

[3] E. Lazaridis and E. M. Drakakis, “Full analogue electronic realisation of the hodgkin-

huxley neuronal dynamics in weak-inversion cmos,” in Engineering in Medicine and

Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the

IEEE, 2007, pp. 1200–1203.

[4] P. Georgiou and C. Toumazou, “A silicon pancreatic beta cell for diabetes,” Biomed-

ical Circuits and Systems, IEEE Transactions on, vol. 1, no. 1, pp. 39–49, 2007.

[5] Z. F. M. Alain Destexhe and T. J. Sejnowski, Kinetic models of synaptic transmis-

sion., 2nd ed., ser. Methods in Neuronal Modeling (2nd ed.). Cambridge, MA: MIT

Press, 1998, pp. 1–26.

70 REFERENCES 71

[6] R. Sarpeshkar, R. F. Lyon, and C. Mead, A low-power wide-linear-range transcon-

ductance ampliﬁer, ser. Neuromorphic Systems Engineering: Neural Networks in

Silicon. Norwell, MA, USA: Kluwer Academic Publishers, 1998, pp. 267–313.

[7] S. P. DeWeerth, G. N. Patel, and M. F. Simoni, “Variable linear-range subthreshold

OTA,” Electronics Letters, vol. 33, no. 15, pp. 1309–1311, 1997.

[8] T. Delbruck, “‘bump’ circuits for computing similarity and dissimilarity of analog

voltages,” in Neural Networks, 1991., IJCNN-91-Seattle International Joint Confer-

ence on, vol. i, 1991, pp. 475–479 vol.1. Chapter 4

ISFET and Iontophoresis Technique

4.1 Introduction

The most important element in the bionics chemical synapse is the chemical front-end; the neurotransmitter sensor. The purpose of this sensor is to function as the receptor of the chemical synapse i.e. to detect the presence of the neurotransmitter. As the processing circuit, used to duplicate the biological functionality, will be implemented using CMOS technology, therefore the sensor for this chemical synapse should also be integrable on the same platform. The ISFET has demonstrated its ability to function as the chemical sensor for this work [1].

In this chapter, the principle of an ISFET will be described in terms of its chemical and mathematical theory. The ISFET’s operation, sensitivity and drift will also be presented. Additionally, the enzyme-modiﬁed ISFET will be introduced as the broad-

72 4.2. ISFET principle 73 speciﬁc chemical sensor due to its capability to sense diﬀerent chemical species. The glutamate ISFET, that will emulate the function of the AMPA and NMDA receptors in the bionics chemical synapse, will be studied and implemented from a commercially- available ISFET.

The second part of this chapter will discuss a fast chemical stimulus technique that will be used to reproduce the neurotransmitter signal. The required one-millisecond chemical perturbation is fulﬁlled by a technique called iontophoresis. Experimental result shows that this technique can generate a fast chemical stimulus with the desired time duration.

4.2 ISFET principle

The origin of an ion-sensitive ﬁeld eﬀect transistor (ISFET) can be traced back to the

1970s with the main contribution made by Piet Bergveld. Initially, the purpose of this compact solid-state chemical sensor was to act as the probe for the monitoring of ionic activities in both electrochemical and biological systems [2]. Since then, the research group at Twente University has published a number of in-depth reports on the characteristics of the ISFET and its applications. The operation and principle of ISFETs and

MOSFETs are similar. An ISFET is a ﬂoating gate MOSFET with an extra insulating membrane. In the case of a MOSFET, its operational regions are determined by its bias potential at the gate while an ISFET requires a reference electrode (Ag/AgCl) as its pseudo-gate for biasing. The change in pH alters the threshold voltage of the ISFET, which can be sensed through the drain current or the gate source potential.

The applications of the ISFET are mainly about pH sensing. However, there have been 4.2. ISFET principle 74 extensive use of ISFETs to sense diﬀerent chemical species, such as glucose [3], urea [4], glutamate [5], creatine [6], acetylcholine [7], γ-aminobutyric acid [8]. The detection of these solutions is carried out by the immobilisation of certain enzymes on top of the gate of the ISFET, called an ENFET. The immobilised enzyme catalyses a chemical reaction with the interested analyte and the product of this reaction can be H+ or OH–, altering the local pH at the membrane layer of the ENFET.

4.2.1 ISFET Operation

To understand how an ISFET operates, it is important to analyse the working mechanisms of a MOSFET. The planar cross section of an ISFET is shown in Fig.(4.1). This diagram is similar to a MOSFET structure with the exception that the gate terminal is

ﬂoating and requires a reference electrode for biasing. In the case of the MOSFET, the current channel through the drain and source is controlled by the gate voltage. In other words, the gate voltage modulates this channel and the source-drain path is blocked where there is no gate bias. However, when the voltage bias at the gate is below the threshold voltage, an exponential relationship between the drain current and the gate potential is observed. Furthermore, if the gate potential is higher than the threshold voltage, this relationship becomes either linear in the triode region or square in the saturation region.

For the ISFET, both the ﬂoating gate voltage (biased via the reference electrode) and the pH of the solution determines the drain current. The eﬀect of the solution’s pH on the drain current of the ISFET is illustrated in Fig.(4.2). The threshold voltage of an

ISFET (Vth(ISFET )) can be expressed in terms of the threshold voltage of a MOSFET

(Vth(MOSFET )) and the grouping of the pH dependent potentials (Vchem), as shown in eq.(4.1). 4.2. ISFET principle 75

Insulating membrane G SiO2

B S D

p+ n+ n+

p-type Si

Figure 4.1: Ion Sensitive Field Eﬀect Transistor

Vth(ISFET ) = Vth(MOSFET ) + Vchem φ (4.1) = E − ψ + x − m + V ref s sol q th(MOSFET )

φ V = E − ψ + x − m (4.2) chem ref s sol q

From eq.(4.1), Eref is the bias potential of the reference electrode, ψs is a pH dependent

φm chemical potential, xsol is the surface dipole potential of the solution and q is the metal work function. The pH sensitivity of the ISFET or the relationship between Vchem, in eq.(4.2), and the pH can be explained with the site-binding theory and the double layer theory. 4.2. ISFET principle 76

pH1 1.5 pH4 pH7 pH10 A) -3

1.0 Drain current (x10

0.5

0.0 0.0 0.2 0.4 0.6 0.8 1.0 Reference electrode voltage (V)

Figure 4.2: Drain current vs. reference electrode potential compared to ground for diﬀerent pH values

4.2.2 ISFET sensitiviy

On top of the ISFET, there is the layer called the passivation layer. Various materials can be used to construct this layer to provide a diﬀerent pH sensitivity and dynamic range [9]. This surface also has the capability to sense positive or negative charges. In other words, the material’s chemical properties can be acidic, alkali or neutral. In this case the bare gate material, silicon dioxide (SiO2), will be discussed in the site binding theory of the ISFET.

– + The reaction between water and silicon dioxide yields SiO , SiOH2 or SiOH. The neutral binding site (SiOH) can donate or accept protons, as shown in eq.(4.3) and

(4.4) where Ka and Kb are the equilibrium chemical constants. 4.2. ISFET principle 77

− + [SiO ][H ]s SiOH )−−−−* SiO− + H+ with K = (4.3) S a [SiOH]

[SiOH+] + −−* + 2 SiOH + HS )−− SiOH2 with Kb = + (4.4) [SiOH][H ]s

To find the ability of silicon dioxide to resist a change in pH, the buffer capacity parameter (β) is defined as:

d[B] β = (4.5) dpHs

– + where [B] is the total surface charge which is [SiO ]+[SiOH2 ] and pHs is the pH at the

SiO2 surface. This total surface charge can be determined from the ratio between the surface charge density and the charge [10], deﬁned as:

−σ [B] = s (4.6) q

The surface charge density is modelled as two layers which are the Stern inner layer and the outer diﬀuse layer [11, 12]. The positive and negative ions in the solution between the gap of these layers form the equivalent capacitor. The relationship between this double layer capacitance (Cdl) and σs is:

σs = ψsCdl (4.7)

ψs is the electrical surface potential. Substituting eq.(4.7) into eq.(4.6): 4.2. ISFET principle 78

−1 [B] = (ψ C ) (4.8) q s dl

Substitute eq.(4.8) into eq.(4.5):

d( −ψsCdl ) β = q dpHs (4.9) −C dψ = dl s q dpHs

Rearrange eq.(4.9):

dψ −qβ s = (4.10) dpHs Cdl

According to the Boltzmann equation, the concentration of hydrogen ions at the surface

+ + [Hs ] and the bulk concentration of hydrogen ions [H ] is given by:

−qψ [H+] = [H+] exp( s ) (4.11) s KT

+ -pHs + -pH Rearrange eq.(4.11) by substituting [Hs ] = 10 and [H ] = 10 :

−qψ s = ln[10(-pHs+pH)] kT (4.12) kT ψ = (ln 10) (pH − pH) s q s

Diﬀerentiate eq.(4.12) with respect to pH: 4.2. ISFET principle 79

dψ kT dpH s = (ln 10) s − 1 (4.13) dpH q dpH

Substitute dpHs = dpHs × dψs into eq.(4.13): dpH dψs dpH

dψ kT dpH dψ s = 2.3 s × s − 1 dpH q dψs dpH kT dψs kT dpHs −2.3 = 1 − 2.3 (4.14) q dpH q dψs kT dψ 2.3 s = q dpH 2.3 kT dpHs − 1 q dψs

Substitute eq.(4.10) into eq.(4.14):

kT dψ 2.3 s = q dpH kT −Cdl 2.3 q ( qβ ) − 1 kT −2.3 q (4.15) = kT Cdl 2.3 q ( qβ ) + 1

= −2.3αUT where α is the sensitivity coeﬃcient = 1 and U is the thermal voltage = kT . kT Cdl T q 2.3 q ( qβ )+1 When α reaches unity, this sensitivity will be 59mV/pH at 300K. The pH sensitivity when α = 1 is deﬁned as the Nernstian sensitivity.

From eq.(4.15), it can be realised that dψs = −2.3αUT dpH or ψs = −2.3αUT pH. With the substitution of this term to eq.(4.2), the simpliﬁed expression [13] for Vchem is: 4.2. ISFET principle 80

2.3αkT V = γ + pH (4.16) chem q where γ is the grouping of the non-pH terms. From eq.(4.15), it can be observed that the buﬀer capacity for the sensing area of the ISFET is a crucial parameter to determine its pH sensitivity. A material with a higher buﬀer capacity provides a greater level of sensitivity. Amongst the common materials used for the ISFET’s passivation layer,

Ta2O5 gives the highest pH sensitivity owing to its high buﬀer capacity [14].

A study was conducted to try to provide a pH sensitivity higher than that of the

Nernstian relationship. By adding a chemical, NaF, into the solution [15], the sensitivity increased to 80-85 mV/pH. However, the dynamic range for this modiﬁcation is narrow

(in the range of pH 4-6).

4.2.3 Reference electrode

Measurements in electrochemistry made by chemical sensors require a stable potential as the reference point. This is achieved using a reference electrode, which provides both a constant potential and an electrical interlink. To comply with this requirement, a silver-silver chloride common reference electrode is typically used. Fig.(4.3) shows the physical appearance of this electrode.

To provide a constant potential, the silver wire, coated with silver chloride, will be sub- merged into a saturated sodium chloride solution (typically 3M). The electrical path is provided through the porous glass (frit). This glass allows an electrical link to be made but obstructs any chemical activities between the inside and outside of the electrode. Y

4.2. ISFET principle 81

Porous Glass (Frit) Saturate KCl

Ag – AgCl wire

Figure 4.3: Ag-AgCl reference electrode

Also, the other function of this frit is to ensure that there is no any mix up between saturated KCl solution inside the reference electrode and outside measured solution.

This separation between the measured solution and the KCl solution in the frit keeps the potential of the Ag-AgCl electrode constant.

Another type of reference electrode, where there is no liquid junction between the reference wire and the environmental solution, is a pseudo reference electrode. Without the reference electrode, the electrical potential of this reference electrode is uncertain.

Practical use of the pseudo reference electrode requires a more complex measurement system. For example, a research group at Twente University under Bergveld employed two ISFETs for measurements with a pseudo reference electrode [16]. One was designated as the measurement ISFET while the other acted as the reference. To obtain the correct electrochemical signal, diﬀerentiation between these two ISFET signals was required. 4.2. ISFET principle 82

Another interesting development in this area is the introduction of a micro reference electrode. In 2003, Huang et al. fabricated a miniature Ag-AgCl reference electrode

[17]. However, this micro reference electrode cannot be integrated using a standard

CMOS process. Lastly, the integrated reference electrode approach was initiated by

Comte et al. in 1978 [18], where another ISFET (REFET) is used as a reference to a measurement ISFET. However, it is diﬃcult to fabricate a fully matched REFET and

ISFET.

4.2.4 Drift in ISFET

One of the non-ideal behaviours that can be found in an electrochemical sensor is the increase or decrease in the measured signal when there is no actual change in the chemical concentration. This slow and uni-directional change is called drift. Drift is classiﬁed into two categories: short- and long-term. Changes of a few mV per hour when an

ISFET has been in contact with the chemical solution for a few hours are considered as the short-term drift [19]. A drift is considered to be a long-term drift when an ISFET has been measuring for at least ten hours; furthermore, this change in the measured signal could possibly be ten times more than in the short-tem drift case [19].

The method for the deposition of the sensing material on an ISFET was reported as one of the factors that aﬀect drift in ISFETs. Hammond et al. reported that drift in his

CMOS ISFET is higher than the post-processed ISFET [20]. His report also explained and compared the physical diﬀerence in the material between a CMOS and a non-CMOS

ISFET. The plasma enhanced chemical vapour (PECVD) deposition method used in

CMOS ISFET is a lower temperature process, which produces non-uniform crystals or a polycrystalline. On the other hand, a non-CMOS ISFET’s sensing membrane is grown in a low pressure/high temperature condition which yields a single crystal. 4.3. Enzyme-Immobilised ISFET 83

There have been many attempts to compensate for drift in an ISFET. All of the drift reduction schemes employed additional electronic circuits to counteract the drift in the signal. This method requires an accurate drift model of the ISFET. However, the drift eﬀect in an ISFET can be neglected if the measurement time duration is less than a minute. This means that a millisecond range chemical perturbation in the experiment of this work will not be aﬀected by both long or short term drift.

4.3 Enzyme-Immobilised ISFET

Typically, an ISFET is designed for pH sensing or the detection of a change in hydrogen concentration. However, the function of an ISFET is not only limited to pH sensing.

There have been many research works that applied ISFETs as a broad-specific chemical sensor, through extra modifications and post-processesing. An ISFET deposited with an ion-selective membrane, which makes it sensitive to a specific ion, is classified as a

CHEMFET.

As mentioned earlier in section 4.2, an ISFET with an extra enzyme layer on top of the sensing membrane is called EnFETs. The function of this enzyme is to catalyse the chemical reaction to either yield extra protons or electrons. This means that the

EnFET measures the ions that are generated as a by-product of the hydrolysis reaction.

The ﬁrst EnFET publication was made by Caras et al. in 1980 [21], where he proposed an EnFET for sensing penicillin. Their penicillin-FET was coated with penicillinase.

An example of an EnFET that will be described here is the EnFET for detecting urea

[22]. 4.3. Enzyme-Immobilised ISFET 84

UREASE + − Urea + 3 H2O −−−−−−→ CO2 + 2 NH4 + 2 OH (4.17)

From eq.(4.17), when urea is hydrolysed with urease as the catalyst, this chemical reaction will give extra OH– ions. The increase in OH– ions leads to a pH change which can be sensed by the EnFET. Table (4.1) summarises the common analytes that can be sensed with an ISFET.

Table 4.1: Common analytes and immobilised enzymes used in EnFET Analyte Immobilised enzyme Local pH change Reference Penicillin Penicillinase decrease [21] Glucose Glucose oxidase decrease [23] Lactate Lactate oxidase decrease [24] Urea Urease increase [22] Creatinine Creatinine deiminase decrease [25] Glutamate Glutamate oxidase decrease [26] γ-Aminobutyric acid GABA oxidase decrease [27] Acetylcholine Acetylcholine esterase decrease [28]

Caras also reported an interesting observation regarding the relationship between the penicillin-FET’s linear range and sensitivity with the buffer capacity of the analyte. In this experiment, it was observed that the Penicillin-ISFET tested in a higher concentration buffer solution had a broader linear range and a lower sensitivity. In contrast, the same EnFET gave a shorter linear range and a higher sensitivity when operated in a lower concentration buffer solution.

4.3.1 Glutamate ISFET

According to the Destexhe’s chemical synapse model [29] on the AMPA and NMDA receptors, the bionics version of these receptors requires a glutamate sensor as the 4.3. Enzyme-Immobilised ISFET 85 chemical input. The glutamate ISFETs used in this work are the Sentron ISFETs

(Sentron BV, the Netherlands) with glutamate oxidase (GLOD) immobilisation. The chemical reaction of glutamate, catalysed by GLOD, is expressed as:

GLOD + Glutamate + O2 + H2O −−−−→ 2− oxoglutarate + NH4 + H2O2

The procedures for this immobilisation follow Braeken et al.’s work [26]. The procedures undertaken to achieved GLOD immobilisation are as follows:

The ISFET’s surface was cleaned with a 3:1 solution of sulfuric acid and hydrogen

peroxide for 15 minutes.

The cleaned ISFET was further treated in a UV/ozone machine for 15 minutes.

After the UV/Ozone treatment, the ISFET was rinsed with ethanol.

The ISFET was heated at 110◦C for 30 minutes on a hot plate.

The ISFET was immersed in a 1:1 solution of Poly-l-lysine (PLL) solution (PLL

4mg/mL in 10mM borate buﬀer pH8) and sodium cynaoborohydride (NaCNBH3) for 30 minutes.

The ISFET was further immersed in a 1:1 V/V solution of glutaraldehyde solution

(GA) and NaCNBH3 for 30 minutes.

GLOD coupling on the ISFETs surface was accomplished by using a pipette to

drop a 300µg/mL GLOD in PBS solution on the ISFET’s surface. The time

duration for this process was overnight.

The overnight GLOD-coupled ISFETs was further treated with CNBH for 30

minutes. 4.3. Enzyme-Immobilised ISFET 86

The second treatment of the ISFETs with CNBH after GLOD coupling is vital for a robust linkage of GLOD [26]. Also, it is advised that these glutamate ISFETs should be kept at 4◦C with a Tris buﬀer (150mM) inside a light tight container [26].

For the bionics chemical synapse, a linear relationship between the ion concentration and the output signal is required. However, a typical voltage-mode ISFET readout circuit

[30] has a logarithmic relationship. Five diﬀerent concentration of HCl (0.5, 1, 1.5, 2 and 2.5mM) were tested with this ISFET readout circuit. The obtained calibration curve, shown in Table 4.2 and Fig.(3.6), exhibits a logarithmic relationship.

Table 4.2: Data of the measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from a voltage-mode readout circuit [30] HCL concentration (mM) Output voltage (mV) 0.5 -789.1 1 -770.5 1.5 -763.3 2 -757.2 2.5 -748.5

This readout circuit was operated in a dual supply ±6V and the reference electrode was biased at 0V. This logarithmic curve can be linearised with the H-cell current mode readout circuit, shown in Fig.(4.5), proposed by Shepherd et al. [31].

In Fig.(4.5), the ISFET current (IISFET ) is the square root function of the ions concentration. The relationship between IISFET and I1 [31] is:

−γ −Vref 0.5 nU U IISFET = I1[IONS] e T e T (4.18)

The translinear loop on transistors P1, P2, P3 and P4 functions as a current squarer. 4.3. Enzyme-Immobilised ISFET 87

-750

-760

-770

Output voltage-780 (mV)

0.5 1.0 1.5 2.0 2.5 HCl concentration (mM)

Figure 4.4: Measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from a voltage-mode readout circuit [30]

The output current of this readout circuit (Iout) when I1 = I2 is:

2γ −2Vref nU U Iout = I1[IONS]e T e T (4.19) where γ is the grouping of all pH-independent chemical potentials, n is the subthreshold parameter of a MOSFET, Vref is the DC potential used for biasing the transistor N1 and the ISFET N2 to operate in weak inversion, [IONS] is the concentration of the interested solution and UT is the thermal voltage. Diﬀerent solutions of HCl as presented in Table 4.2 were measured using the ISFET with this current mode readout circuit.

The measured results are shown in Table 4.3 and Fig.(4.6).

The trend line in Fig.(4.6) shows a linear relationship between the concentration of HCl and the output current. This trend line has an r-square parameter of 0.987. The ISFET

(N2) and the transistors (P1, P2 and P4) were biased at 0.44V. Y

4.3. Enzyme-Immobilised ISFET 88

P1 I2 P3 P2 P4

I1 Iout

I ISFET N1 N2

Vref

Figure 4.5: Current mode ISFET readout circuit which exhibits a linear relationship between the output current and the concentration of analyte

When this readout circuit is applied to the glutamate ISFET measuring five different glutamate concentration solutions (0.5, 1, 1.5, 2 and 2.5mM), a linear relationship between the glutamate concentration and the output current was also obtained, as shown in Table 4.4 and Fig.(4.7). The glutamate solutions were prepared from L-glutamatic acid (Sigma, UK) in a phosphate buffer saline solution (PBS, 10mM, pH 7).

The R2 of the trend line curve in Fig.(4.7) is 0.9957, indicating a good ﬁt to the data.

It was observed that the glutamate ISFET required a higher voltage bias than an ordinary ISFET. This phenomena can be explained by the extra glutamate oxidase layer 4.3. Enzyme-Immobilised ISFET 89

Table 4.3: Data of the measured results for diﬀerent HCl concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.44V HCL concentration (mM) Output current (nA) 0.5 14.12 1 31.23 1.5 44.52 2 56.53 2.5 63.66

30 Output current (nA) 20

0.5 1.0 1.5 2.0 2.5 HCl concentration (mM)

Figure 4.6: Measured results for diﬀerent HCL concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.44V on top of the ISFET’s sensing membrane, which forms an extra equivalent capacitor.

This capacitor divides the bias potential from the reference electrode to the source of the glutamate ISFET.

From the measured results in Table. 4.4, it would be useful if the value of γ in eq.(4.19) can be extracted for further use. Eq.(4.19) can be rearranged to give: 4.3. Enzyme-Immobilised ISFET 90

Table 4.4: Data of the measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.26V Glutamate concentration (mM) Output current (nA) 0.5 51.19 1 56.29 1.5 60.05 2 63.59 2.5 67.71

66 64 62 60 58 56 Output current (nA) 54 52

0.5 1.0 1.5 2.0 2.5 Glutamate concentration (mM)

Figure 4.7: Measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.26V

! nU I 1 γ = T ln out × (4.20) 2 [IONS] −2Vref U I1e T

Iout [IONS] is the sensitivity of the current mode readout circuit (nA/mM). To verify the calculated γ, another set of measured results with a diﬀerent bias voltage (Vref ) were gathered. This is shown in Table 4.5 and Fig.(4.8).

2 Iout From Fig.(4.8), the R is 0.9947. The sensitivities ( [IONS] ) are 142.04nA/mM and 4.3. Enzyme-Immobilised ISFET 91

Table 4.5: Data of the measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.21V Glutamate concentration (mM) Output current (nA) 0.5 403.59 1 472.89 1.5 541.25 2 601.15 2.5 694.56

650

600

550

500 Output current (nA) 450

400

0.5 1.0 1.5 2.0 2.5 Glutamate concentration (mM)

Figure 4.8: Measured results for diﬀerent glutamate concentration of 0.5, 1, 1.5, 2 and 2.5mM from the current mode readout circuit in [31] when Vref = 0.21V

8.068nA/mM when Vref are 0.21V and 0.26V, respectively. Using eq.(4.20), the gamma parameters are 400.223 and 416.822 for Vref = 0.21V and 0.26V, respectively. It should be noted here that there were five ISFETs which immobilised in this work. Each glutamate ISFET was tested to find its calibration curve. The results shown in this section were extracted from the highest sensitivity glutamate ISFET among these five glutamate ISFETs. 4.4. Coulometric titration 92

The combination of the modified ISFET, for glutamate sensing, and the current-mode readout circuit, operated in the weak inversion region, exhibit excellent linearity between the output current and the concentration of glutamate. Furthermore, the sensitivity of this sensor system is tunable, as confirmed by the measured results. Therefore, it can be concluded that this is the first linear and sensitivity-controllable electrochemical sensor for glutamate.

4.4 Coulometric titration

The research group at Twente University reported a method that can be used to create a fast ion concentration change suitable for ISFET sensing in the 1980s. The flow injection, the first technique, was implemented with a high speed pump and valve, where two different pH solutions were pumped to the sensing membrane of the ISFET. This report

[32] indicated that both the ascent rate of the pH gradient and the buﬀer capacity have an inﬂuence on the response time.

Another approach to create a rapid ionic perturbation, reported by Bergveld’s group, is the coulometric titration technique. This technique requires two electrodes and a current source. The generating electrode (anode) produces H+ ions while the counter electrode (cathode) yields OH– ions. The chemical reaction, the oxidation and reduction of water molecule, at these two electrodes and the diagram of this technique are shown in eq.(4.21) and Fig.(4.9), respectively.

+ − 1 At anode (generating electrode) : H2O → 2 H + 2 e + O2 2 (4.21) 1 At cathode (counter electrode) : H O + e− → OH− + H 2 2 2 4.4. Coulometric titration 93

Current source

Generating electrode Counter electrode

+ H - H+ OH OH- + - H+ H OH OH-

Figure 4.9: Diagram of coulometric titration

For a constant applied current (I), the concentration (C) of the species added into the solution by the generator will be directly proportion to the applied period (t). The coulometric relationship [16] is given by eq.(4.22).

It C = (4.22) nF A where n is the number of moles of e– in the reaction, F is the Faraday’s constant, A is the surface area of the generator. When H+ ion generation at the anode and the OH– ions generate at the cathode occur in a separated system with no mixing and titration between the generated H+ and OH– ion generation, the change in H+ ions observed by the ISFET is only inﬂuenced by the H+ ion concentration produced at the generator.

As a result, a larger signal can be observed by the ISFET after a longer generation period.

As diffusion is the main mechanism for the movement of the H+ and OH– ions in the bulk solution, the maximum time response and the thickness of a diffusion layer can be estimated. The maximum time response determines how fast the ions can diffuse to a 4.4. Coulometric titration 94 certain distance (i.e. the thickness of a diffusion layer). The relationship between time and distance of the diffusion phenomena [11] can be described according to the eq.(4.23).

√ L = 2Dt (4.23) where L is the thickness of a diﬀusion layer, t is time response and D is the diﬀusion

−9 coefficient. In the case of the hydrogen ions, the diffusion coefficient (DH+ ) is 9.3×10 m/s2 [33]. If the required minimum response time is assumed as 1ms, the maximum distance between the generated electrode and the sensor should be less than or equal to

4.31 µm. The signiﬁcance of this equation is that it can be used as an estimate of the maximum time response when the distance is known.

Furthermore, other chemical species can also be produced using the coulometric technique. For instance, Ag+ can be induced by using a silver wire as the generating electrode, and similarly, by using a mercury wire as the generating electrode, Hg2+ can be produced.

It should be noted that the coulometric titration technique can only be implemented where the titrant is in the form of a solid metal. This is because electrical current is central to this ion generation process. If the interested titrant is not in the rigid form and has no electrical conductivity, this technique will not be applicable. 4.5. Iontophoresis method 95

4.5 Iontophoresis method

In the case where the titrant is not in the solid form and has no electrical conductivity, iontophoresis is an alternative approach to create an ion ﬂow in a similar way to the coulometric titration method. The iontophoresis technique has been extensively used to conduct experiments in neurological studies especially in the delivery of neuroactive substances. The diagram of this technique is shown in Fig.(4.10).

Figure 4.10: Diagram of iontophoresis

The ejection of ions in this technique is controlled by the applied current via the current source. A positive current (i.e. electrode 1 has a positive potential) will repel the posi- 4.5. Iontophoresis method 96 tive ions out of the micropipette while the negative ions will be attracted to electrode 1.

In the case shown in Fig.(4.10), a positive current is applied to the HCl solution which causes H+ ions to ﬂow out of the micropipette. The diameter of the micropipette tip should be around 1 µm or less to decrease the probability of the incontinent diﬀusion and to achieve a low tip potential [34].

As the implementation of a silicon chemical synapse [35] requires a fast neurotransmitter test stimulus, the iontophoresis technique can be used to emulate the ion ﬂow when the neurotransmitter is released, for instance: the ﬂow of glutamate can be achieved by using sodium glutamate as the solution in the micropipette with a negative applied current.

The quantity of ions released from the tip of the micropipette can be described by Fick’s law of diﬀusion, shown in eq.(4.24).

r C(r, t) = C(0) erfc √ (4.24) 2 Dt where C is the concentration of the ejected ions, r is the distance, t is time duration,

C(0) is the concentration at the position of x = 0 and D is the diﬀusion coeﬃcient.

C(0) in eq.(4.24) can be modiﬁed [36, 37] to give:

in r C(r, t) = erfc √ (4.25) F 4πDr 2 Dt 4.5. Iontophoresis method 97 where i is an electrical current, F is the Faraday constant and n is the transport numbers. To simplify the complement error function (erfc), let’s consider the transformation of the complement error function shown in eq.(4.26)

2 ∞ e−a X (−1)n · (2n − 1)!! erfc(a) = √ (4.26) a π (2a2)n n=0

Substitute a = √r into eq.(4.26): 2 Dt

2 − √r ∞ r e 2 Dt X (−1)n · (2n − 1)!! erfc √ = √ n (4.27) 2 Dt r 2 √ π n=0 2 √r 2 Dt 2 Dt

Substitute eq.(4.27) into eq.(4.25):

2 − √r ∞ in e 2 Dt X (−1)n · (2n − 1)!! C(r, t) = n (4.28) F 4πDr r √ 2 √ π n=0 2 √r 2 Dt 2 Dt

Rearrange eq.(4.28):

q in t ∞ n 2F r2 Dπ3 X (−1) · (2n − 1)!! C(r, t) = (4.29) 2 2n r r exp 4Dt n=0 2 √ 2 Dt

Considering only the coeﬃcient terms in eq.(4.29) 4.5. Iontophoresis method 98

in q t 2F r2 Dπ3 C(r, t) ∝ (4.30) r2 exp 4Dt

From the eq.(4.30), it can be observed that the concentration (C) is directly proportional to the current (i) and time duration (t), while the distance (r) is inversely proportional to the concentration (C).

Likewise, by only considering eq.(4.25) and neglecting the complementary error function (erfc), the transport number (n) can be related to the time duration of the injected current (t). The transport numbers (or transference numbers) of the ion x is deﬁned as the fraction of the ion x’s conductivity over the whole conductivity [11], shown in eq.(4.31).

|z |u C n = x x x (4.31) x X |zj|ujCj j

where nx is the transport number of the ion x, zx is the magnitude of charge of the ion x, ux is the mobility of the ion x, and Cx is the concentration of the ion x. From eq.(4.31), the transport number is directly proportional to the magnitude of the charges.

nx ∝ zx (4.32)

Additionally, the deﬁnition of the charges is described as a product of the current (I) and time (t). 4.6. Experimental results on Iontophoresis 99

zx = It (4.33)

From eq.(4.32) and eq.(4.33), it can be concluded that the transport number is directly proportional to time.

nx ∝ zx ∝ t (4.34)

The neurotransmitter signal in Destexhe’s chemical synapse model [29] is expressed as a

1ms duration pulse with a 1mM amplitude. To create an ionic perturbation within the millisecond range, a calculation based on the iontophoresis technique [37] is carried out.

Recalling that the amount of ion concentration ejected at the tip of the micropipette is given by eq.(4.25). Eq.(4.25) can be re-arranged to give:

4F Dπr[IONS] i = (4.35) n × erfc( √r ) 2 Dt

Therefore, the required current (i) to generate a 1mM glutamate ([IONS]) change with a 1ms time duration (t) where the diﬀusion of glutamate (D) = 2.5×10−10 m2/s [38], the distance (r) = 2µm, the transport number of glutamate (n) = 0.4 [38] and the

Faraday’s constant (F ) = 96485.3399 C/mol, is 0.4µA.

4.6 Experimental results on Iontophoresis

The system used for the iontophoresis experiment is shown in Fig.(4.11). This system consists of three main parts: the glass micropipette with 1µm diameter (from World

Precision Instrument Ltd), two platinum electrodes (Pt1 and Pt2), the AC current 4.6. Experimental results on Iontophoresis 100

source (Keithley model 6221), the ISFET from Sentron Europe B.V. and the opamp

driven readout circuit [39]. In the experiment, the 3M HCl solution was put into the

micropipette for H+ perturbation. The bulk solution used in this case was the Phos-

phate Buﬀer Saline (PBS).

HCl solution

- Cl Cl- Cl- Micropipette Cl- Cl- Cl- Cl- Cl-

Cl- PT1

Cl- Cl- Keithley 6221 AC + + Sentron ISFET H H + Current source H+ H+

+ + + Reference H H H H+ H+ H+ H+ electrode - G1 G2 Current source

PT2

Generating electrode Counter electrode

+ H - H+ OH - OH - OH- OH Figure 4.11: System used for iontophoresis experiment

As there was no stirring in this experiment, the convection eﬀect would not inﬂuence the HCl solution movement of the ejected ions. The change in proton concentration measured through the

- Cl Cl- Cl- ISFET and the readout circuit was a local ion change only; and diﬀusion was assumed Cl- Cl- Cl- Cl- Cl- as the only contribution to the ion concentration ﬂuctuation. The distance between Cl- Electrode1

Micropipette Cl- Cl- the ISFET and the tip of micropipette is in order of 1µm; this was controlled by the

H+ H+ + micromanipulator. The current source was kept ﬂoating to separate the ground of the H+ H+ H+ H+ H+ H+ H+ H+ H+ current source from the ground of the readout circuit. This ﬂoating current source also - Current source ensured that there was no leakage current through the reference electrode. Chemical Sensor

Electrode2 4.6. Experimental results on Iontophoresis 101

The ﬁrst part of this experiment was to determine the relationship between the current amplitude and the concentration change. A current pulse signal with a 1ms pulse width was used with the amplitude set at -1µA, +0.6µA and +1.0µA. The result from this experiment is shown in Fig.(4.12).

Figure 4.12: Measured result for three diﬀerent injected amplitudes at 1µm distance between the micropipette tip and the ISFET’s surface (insert is a ’Zoom in’ of one period of the measured result)

It can be observed from the result in Fig.(4.12) that the greater the amplitude of the current injected (i), the larger the ion concentration (C) sensed by the ISFET, which is consistent with eq.(4.30). The negative current test at -1µA amplitude, as expected, did not produce a response. This is correct because the ISFET is only sensitive to changes in proton or H+ ions and not Cl– ions. 4.6. Experimental results on Iontophoresis 102

A 1µA current signal with three diﬀerent pulse widths (10ms, 1ms and 0.1ms) were used as the input signal for the second experiment. The result is shown in Fig.(4.13).

Figure 4.13: Measured result for three diﬀerent current pulse widths at a ﬁxed injected amplitude of 1uA and a 1µm distance between the micropipette tip and the ISFET’s surface (insert is a ’Zoom in’ of one period of the measured result)

It can be seen that the ISFET responded to a 1ms pulse but it could not detect a

0.1ms pulse. With a longer injection time, a larger response was obtained, which agrees with the eq.(4.30). Satisfactory repeatability of the response was observed in successive proton injections. From the measured results shown earlier, it can be concluded that this experiment is the ﬁrst iontophoresis technique to create and verify a millisecond

H+ perturbation on the ISFET. 4.7. Summary 103

4.7 Summary

This chapter has presented the basic concepts of an ISFET such as its operation, sensitivity, which was explained by chemical theories, and drift, one of its imperfections.

The ISFET’s ability to detect diﬀerent chemical species by modifying the sensing area, an ENFET, has been described. Furthermore, the modiﬁcation procedures to produce the glutamate ISFET has been given. The non-linear relationship between the concentration of hydrogen ions and the output signal of the traditional voltage-mode readout circuit [30] have been discussed in this chapter. The required linear relationship between the ion concentration and the output signal can be achieved with a recent current-mode readout circuit [31].

The later sections of this chapter examined, in particular, the iontophoresis chemical perturbation technique for the generation of the neurotransmitter test signal in the

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[34] U. Windhorst and H. Johansson, Modern techniques in neuroscience research.

Berlin; New York: Springer, 1999.

[35] S. Thanapitak and C. Toumazou, “Towards a bionic chemical synapse,” in Circuits

and Systems, 2009. ISCAS 2009. IEEE International Symposium on, 2009, pp.

677–680.

+ [36] H. D. Lux and E. Neher, “The equilibration time course of [K ]0 in cat cortex,” Experimental Brain Research, vol. 17, pp. 190–205, 1973.

[37] E. Neher and H. D. Lux, “Rapid changes of potassium concentration at the outer

surface of exposed single neurons during membrane current ﬂow,” The Journal of

general physiology, vol. 61, no. 3, pp. 385–399, March 01 1973.

[38] A. Herz, W. Zieglgnsberger, and G. Frber, “Microelectrophoretic studies concern-

ing the spread of glutamic acid and GABA in brain tissue,” Experimental brain

research, vol. 9, no. 3, p. 221, 1969. REFERENCES 109

[39] H. Nakajima, M. Esashi, and T. Matsuo, “The pH-response of organic gate ISFETs

and the inﬂuence of macro-molecule adsorption,” J. Chem. Soc. Jpn., vol. 10, pp.

1499–1508, 1980. Chapter 5

Bio-inspired Chemical Synapse

5.1 Introduction

Electronic circuits that can mimic the models of a neuron’s membrane potential are well-established in the ﬁeld of biomimetic systems. The objective of these implementations is to create electronic circuits that behave in the same way as a living neuron.

Since the emergence of neuromorphic engineering in the 1980s, there has been an extensive amount of studies and reports on the implementation of neuronal models in silicon integrated circuit. Examples of this are the conductance-based model implementation

(Hodgkin and Huxley model) in [1], the CA3 neuron model on the hippocampal system

[2] and the Beta cell model of the pancreas [3].

Additionally, according to Destexhe’s chemical synapse model, there are four type of postsynaptic receptors: AMP A, NMDA, GABAA and GABAB. The postsynaptic current measured from the whole cell recording of each receptor is shown in Fig.(5.1)

[4]. The AMP A and NMDA receptors will chemically bind with glutamatic acid,

110 5.1. Introduction 111

while γ-aminobutyric acid is the neurotransmitter that can be detected by the GABAA and GABAB receptors. The proﬁle of the neurotransmitter release from the chem-

ical synapse model of Dextexhe et al. [4] is assumed to be the brief pulse of 1mM

Metho ds in Neuronal Mo deling Chapter concentration in amplitude and 1ms in duration.

ABAMPA NMDA

100 pA 20 pA

10 ms 200 ms

CD GABAA GABAB

10 pA 10 pA

10 ms 200 ms

Figure 5.1: Postsynaptic current of (A) AMP A receptor, (B) NMDA receptor, (C)

GABAA receptor and (D) GABAB receptorFigure [4]

Best ts of simplied kinetic mo dels to averaged p ostsynaptic currents obtained from wholecell recordings

A AMPAkainatemediated currents B NMDAmediated currents C GABA mediated currents D

GABA mediated currents For all graphs averaged wholecell recordings of synaptic currents noisy

traces identical description as in Fig are represented with the b est t obtained using the simplest

kinetic mo dels continuous traces Transmitter time course was a pulse of mM and ms duration in all

In this thesis, ancases implementation A mo died from Destexhe of et al the c Destexhe’s C mo died from Destexhechemical et al a synapse D mo died model from will be pre- Destexhe et al tting pro cedures describ ed in App endix B sented via a current-mode CMOS integrated circuit that operates in the weak inversion region. By operating the CMOS integrated circuit in this region, we can both achieve a low power consumption and the direct arithmetic computation. The main focus of this work will be the electronic circuit realisation of the four postsynaptic receptor types in the Destexhe’s chemical synapse: AMP A, NMDA, GABAA and GABAB. The author sincerely believes that this work will ultimately pave the way for the creation of 5.2. Neural bridge 112 an artiﬁcial chemical synapse receptor which has the capability to sense actual neurotransmitter releases from the living neurons.

The sensor for the detection of glutamate (i.e. AMP A and NMDA receptors) is the enzyme-immobilised ISFET with glutamate oxidase. The artiﬁcial glutamate stimulus that represents a neurotransmitter release is established with a micro-tip glass electrode, based on the iontophoresis technique, described in the previous chapter. Due to the diﬃculty in accessing the GABA oxidase enzymes to make a γ-aminobutyric acid

ISFET, the GABAA and GABAB silicon synapse receptors will be veriﬁed electronically without a chemical interface.

This chapter will begin by giving two examples of a silicon neuron that has been used to re-connect a broken neural signal path, which gave rise to the idea of a neural link or a neural bridge. The following sections will then describe the implementation of each of the chemical synapse receptors in the Destexhe model [4]. Towards the end of this chapter, the CMOS implementation of the full postsynaptic circuit that combines the

Hodgkin-Huxley neuron circuit and the bio-inspired chemical synapse will be presented.

5.2 Neural bridge

Since the implementation of an electronic cochlear in the late 1980s [5], bio-inspired circuits on the CMOS platform, have been employed in many applications. One of the interesting applications is in neural prosthetic device or neural interfacing. The ultimate aim of these devices is to replace the damaged or malfunction neurons. This device is considered as a neural bridge which can be used to re-connect a break in the normal neural signal path. Two examples of this neural bridge will be shown here. 5.2. Neural bridge 113

5.2.1 Non-invasive neuron stimulus

Fig.(5.2) shows the diagram of the silicon synapse chip based on the experiment of Kaul et al. [6]. The idea of this experiment is to connect an electronic circuit and live neurons together using a non-invasive neuron stimulation. When neuron A (a presynaptic cell) is stimulated with a signal via a capacitor (C), the excited neuron A will expel the neurotransmitter agents that will be detected by the neuron B (a postsynaptic cell).

The membrane signal of the neuron B, according to this neurotransmitter change, will be sensed via the gate of the transistor. The presynaptic neuron is the visceral dorsal 4

(VD4) and the postsynaptic neuron is the left pedal dorsal 1 (LPeD1). These neurons were obtained from a pond snail.

Electrolyte

Neuron A Neuron B

Oxide

C S D G Semiconductor

Figure 5.2: A diagram based on Kaul’s experiment

5.2.2 Hippocampal neural bridge Sensory Mortor A B C Bergeri etnp al.ut [2] proposed the idea to use an integrated circuit to replace a damagedoutput neuron. This is an example of a neural bridge that bypasses and reroutes the neural signal. The neuron substitution idea is illustrated in Fig.(5.3). Sensory Mortor input A B C output

VLSI circuit

CA1

Dentate

CA3

Dentate CA1

VLSI circuit Electrolyte

Neuron A Neuron B

Oxide

C S D G Semiconductor

5.2. Neural bridge 114

Sensory Neuron Neuron Neuron Motor input A B C output Electrolyte

Neuron A Neuron B

Sensory Neuron Neuron Neuron Motor input A B C output

Oxide

C S D G Semiconductor

VLSI circuit

Sensory Mortor Figure 5.3:input A circuitA diagram forB replacing aC dysfunctionoutpu centralt brain region with a VLSI system

Sensory Mortor input A B C output In this implementation, the central nervous system (CNS) neurons in the hippocampus CA1 region of the brain will be partially replaced by a silicon neuron. Fig.(5.4) shows the slice view of the hippocampus which is comprised of dentate, CA1 and CA3 subregions.

The ﬂow direction of the neuralVLSIsignal circuit in this area startsDent fromate the dentate to CA3 and CA1 respectively. CA3

CA1

Dentate Dentate CA1 CA3

VLSI circuit Figure 5.4: Diagram of the trisynaptic circuit of the hippocampus

In Berger’s work,De thenta physiologicalte properties of the CA3 neuronCA were1 modelled math-

VLSI circuit Electrolyte

Neuron A Neuron B

Oxide

C S D G Semiconductor

Sensory Mortor input A B C output

VLSI circuit

CA1

Dentate 5.3. Implementation of chemical synapse receptor 115 ematically andC implementedA3 with a VLSI circuit. This circuit was used in the place of a normal CA3 neuron, as shown in Fig.(5.5).

Dentate CA1

VLSI circuit

Figure 5.5: Conceptual representation of replacing the CA3 with a VLSI model

The following section will present the implementation of a chemical synapse based on the model of Destexhe et al. [4]. The AMPA and NMDA receptors will employ the glutamate ISFET as the chemical front-end to sense the glutamate concentration change.

This glutamic stimulus, that represents the neurotransmitter signal, will be created via the iontophoresis technique, which was described in Chapter 4.

5.3 Implementation of chemical synapse receptor

In this section, the kinetic model of the Destexhe’s chemical synapse, which was described in section 2.4.3, will be implemented for all four synapse receptors (i.e. AMP A,

NMDA, GABAA and GABAB) using current-mode weak inversion CMOS circuits.

5.3.1 AMP A receptor

From the kinetic model for the post-synaptic transmission [4], the relevant equations for the AMP A receptor are: 5.3. Implementation of chemical synapse receptor 116

dr AMP A = α [T ](1 − r ) − β r (5.1) dt AMP A AMP A AMP A AMP A I = g r (V − E ) (5.2) AMP A AMP A AMP A AMP A

6 where αAMP A = 1.1 × 10 , βAMP A = 190, [T ] is the pulse shape of the neurotransmitter signal with a time duration of 1ms and an amplitude of 1mM, while g = 0.1nS. AMP A

The implementation of the circuit to mimic the rAMP A variable was accomplished by using the Bernoulli cell. For the case of the AMP A receptor, the Bernoulli integrator circuit shown in Fig.(5.6) has the following parameters that corresponds to eq.(5.3).

I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

Figure 5.6: Bernoulli cell circuit used for implementing variable rAMP A

IdAMP A = C nUT (αAMP A[T ] + βAMP A) AMP A I0NMDA (5.3) IinNMDA Iout-NMDA IinAMP A = CAMP A nUT [T ]αAMP A

where n is the subthreshold parameter and UT is the thermal voltage. Let CAMP A =

1.5nF, so the pulse current (IinAMP A), a 1ms pulse, has a peak value at 55nA; and IdNMDA

I0NMDA CNMDA

ISig

A V1 Sig ASig V2

Iout-Sig

I0GABA I A inGABAA Iout-GABA A

I dGABAA I0GABA C A GABAA

I I0u I 02 I in2 I out2 rGABA B

Id3 Iin2+Id2 I02 I0u C2 C3

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

I04 Iin4 Iout4

Id4 I04 5.3. Implementation of chemical synapse receptor 117

the DC current, CAMP A nUT βAMP A = 9.5nA. The output current of this Bernoulli cell

(Iout−AMP A) is shown in eq.(5.4).

Iout−AMP A = rAMP AI0AMP A (5.4)

5.3.2 NMDA receptor

For the NMDA receptor, the ﬁrst order kinetic model and the synaptic current are given by:

dr NMDA = α [T ](1 − r ) − β r (5.5) dt NMDA NMDA NMDA NMDA I = g B(V )r (V − E ) (5.6) NMDA NMDA NMDA NMDA

4 where αNMDA = 7.2 × 10 , βNMDA = 6.6, [T ] is the pulse shape of the neurotransmitter signal with a time duration of 1ms and an amplitude of 1mM, while g = 0.1nS. NMDA For the case of the NMDA receptor, the Bernoulli integrator circuit shown in Fig.(5.7) has the following parameters that corresponds to eq.(5.7).

IdNMDA = C nUT (αNMDA[T ] + βNMDA) NMDA (5.7)

IinNMDA = CNMDA nUT [T ]αNMDA

Let CNMDA = 22nF, so the pulse current (IinNMDA), a 1ms pulse, has a peak value at

52.8nA; and the DC current, CNMDA nUT βNMDA = 4.84nA. The output current of this

Bernoulli cell (Iber−NMDA) is shown in eq.(5.8). I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

5.3. Implementation of chemical synapse receptor 118

I0NMDA IinNMDA Iber-NMDA

IdNMDA

I0NMDA CNMDA

Figure 5.7: Bernoulli cell circuit used for implementing variable rNMDA

ISig

Iber−NMDA = rNMDAI0NMDA (5.8)

Recall eq.(5.6), the parameter B(V ) is required to calculate the current INMDA. The parameter B(V ) was implemented by a sigmoid circuit in Fig.(5.8). The B(V ) param- A V1 Sig ASig V2 eter is shown in eq.(5.9).

Iout-Sig 1 B(V ) = 2+ (5.9) (−62V )[Mg ]o 1 + exp 3.57

2+ where the intracellular of the magnesium concentration ([Mg ]o) is 1mM. The output current as the voltage function of this sigmoid circuitI0GA isB givenA by: I A inGABAA Iout-GABA A

ISig IOut−Sig = (5.10) ASig(V1−V2) 1 + exp{ 2 } (np+np)UT I dGABAA If the terminal V1 is grounded and the output current from the NMDA Bernoulli cell I0GABA C A GABAA

I I0u I 02 I in2 I out2 rGABA B

Id3 Iin2+Id2 I02 I0u C2 C3

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

I04 Iin4 Iout4

Id4 I04 I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

I0NMDA IinNMDA Iout-NMDA

IdNMDA

I0NMDA CNMDA

5.3. Implementation of chemical synapse receptor 119

ISig

A V1 Sig ASig V2

Iout-Sig

Figure 5.8: Sigmoid circuit for B(V ) implementation

in Fig.(5.7) is used as the input current for the sigmoid cell (Isig), hence:

I r 0NMDA NMDAI0GABA IOut−Sig = A (5.11) IinGABA ASig(−V2) A 1 + exp { 2 } Iout-GABA (np+np)UT A

5.3.3 GABAA receptor

From the kinetic modelI for post-synaptic transmission [4], the relevant equations for the dGABAA GABAA receptor are: I0GABA C A GABAA dr GABAA = α [T ](1 − r ) − β r (5.12) dt GABAA GABAA GABAA GABAA

IGABA = g rGABA (V − EGABA ) (5.13) A GABAA A A

where α = 5.3 × 105, β = 180, [T ] is the pulse shape of neurotransmitter GABAA GABAA I I0u I 02 I in2 I out2 rGABA B

Id3 Iin2+Id2 I02 I0u C2 C3

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

I04 Iin4 Iout4

Id4 I04 I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

I0NMDA IinNMDA Iout-NMDA

IdNMDA

I0NMDA CNMDA

ISig

5.3. Implementation of chemical synapse receptor 120

A signal with a time durationV1 of 1msSig and an amplitudeA ofSig 1mMV and2 g = 0.1nS. The GABAA implementation of the circuit to mimic the rGABA variable was accomplished by the Iout-Sig A Bernoulli cell. For the case of the GABAA receptor, the Bernoulli integrator circuit shown in Fig.(5.9) has the following parameters that corresponds to eq.(5.14).

I0GABA A IinGABAA Iout-GABA A

I dGABAA I0GABA C A GABA A

Figure 5.9: Bernoulli cell circuit used for implementing variable r GABAA

I02 I0u Iin2 IdGABA = CGABA nUT (αGABAA [T ] + βGABAA ) Iout2 A A I (5.14) rGABA B I = C nU [T ]α inGABAA GABAA T GABAA where n is the subthreshold parameter and U is the thermal voltage. Let C = T GABAA Id3 820pF, so the pulseIin2+I currentd2 (IinGABAA ), a 1ms pulse, has a peak value at 136.6nA; and I02 I0u the DC current, C nUT βGABA = 4.92nA. The output current of this Bernoulli GABAA C2 A C3 cell (Iout−GABAA ) is shown in eq.(5.15).

Iout−GABAA = rGABAA I0GABAA (5.15)

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

I04 Iin4 Iout4

Id4 I04 5.3. Implementation of chemical synapse receptor 121

5.3.4 GABAB receptor

For the GABAB receptor, the ﬁrst order kinetic models and the synaptic current are given by:

dr GABAB = K [T ](1 − r ) − K r (5.16) dt 1 GABAB 2 GABAB du = K r − K u (5.17) dt 3 GABAB 4 u4 I = g (V − E ) (5.18) GABA GABA 4 GABA B B u + Kd B

4 −1 −1 −1 −1 −1 where K1 = 9 × 10 M s , K2 = 1.2s , K3 = 180s , K4 = 34s , n = 4,

4 Kd = 100µM ,[T ] is the pulse shape of the neurotransmitter signal with a time duration of 1ms and an amplitude of 1mM and g = 0.1nS. GABAB

Implementation of the r and u variables required two Bernoulli cells in cascade as GABAB shown in Fig.(5.10). The ﬁrst Bernoulli cell creates the variable r . The relevant GABAB design equations for this variable are:

Id1 = C1nUT (K1[T ] + K2)

Iin1 = C1nUT [T ]K1 (5.19) I = r I rGABAB GABAB 01

Let C1 = 22nF. The pulse current(Iin1) with 1ms pulse width has the maximum current amplitude at 381nA and the DC current, C1nUT K2 = 1nA. The variable u was generated by the second Bernoulli cell. The ﬁrst order diﬀerential equation of the second Bernoulli cell is given by eq.(5.20). I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

I0NMDA IinNMDA Iber-NMDA

IdNMDA

I0NMDA CNMDA

ISig

A V1 Sig ASig V2

Iout-Sig

I0GABA I A inGABAA Iout-GABA A

I dGABAA I0GABA C A GABAA 5.3. Implementation of chemical synapse receptor 122

I I0u I 01 I in1 I out-u rGABA B

Id2 Id1 I01 I0u C1 C2

Figure 5.10: Bernoulli cell circuit used for implementing variables r and u GABAB

dI I 1 Iin3 out−u Iind32 Iin3 Iin3 + ( ) · Iout−u = ( ) · IrGABAB (5.20) dt C2nUT C2nUT Iout3

Substitute I = r I from eq.(5.19) into eq.(5.20): rGABAB GABAB 01

dIout−u Id2 I01 + ( ) · Iout−u = ( ) · rGABA (5.21) dt IC2nUT Iin3C2nUT Iin3B I in3 d3 Id3 Id3 Rearrangement of eq.(5.17) yields:

du + K u = K r (5.22) dt 4 3 GABAB

By comparing eq.(5.21) and eq.(5.22), the parameters for the second Bernoulli cell are:

I04 Iin4 Iout4

Id4 I04 I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

I0NMDA IinNMDA Iout-NMDA

IdNMDA

I0NMDA CNMDA

ISig

A V1 Sig ASig V2

Iout-Sig

I0GABA I A inGABAA Iout-GABA A

I dGABAA I0GABA C A GABAA

I I0u 5.3.I Implementation of chemical02 synapse receptor I 123 in2 I out2 rGABA B

Id2 = C2nUT K4

I01 = C2nUT K3 (5.23) Id3 Iin2+Id2 I = I u I02 out−u 0u I0u C2 C3 4 Let C2 = 10nF, so Id2 = 11nA and I01 = 33nA. To create the variable u , the translinear current multiplication circuit shown in Fig.(5.11) is required.

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

Figure 5.11: Translinear current multiplication circuit

The relationship between the input current (Iin3) and the output current (Iout3) is shown in eq.(5.24).

I04 I 4 in4 (Iin3) Iout4 Iout3 = 3 (5.24) (Id3)

Let Iin3 = Iout−u and Id3 = I0u, thus:

Id4

I04 4 Iout3 = I0uu (5.25) I0AMPA IinAMPA Iout-AMPA

IdAMPA

I0AMPA CAMPA

I0NMDA IinNMDA Iout-NMDA

IdNMDA

I0NMDA CNMDA

ISig

A V1 Sig ASig V2

Iout-Sig

I0GABA I A inGABAA Iout-GABA A

I dGABAA I0GABA C A GABAA

I I0u I 02 I in2 I out2 rGABA B

Id3 Iin2+Id2 I02 I0u C2 C3

Iin3 Iin3 Iin3 Iin3 Iout3

I Iin3 Iin3 I in3 d3 Id3 Id3

5.4. Implementation of the postsynaptic transmission 124

u4 The term 4 can be established by the translinear loop circuit shown in Fig.(5.12). u +Kd

I04 Iin4 Iout4

Id4 I04

u4 Figure 5.12: Circuit implementation of function 4 u +Kd

The relationship between the output current (Iout4) and the other three input currents

(Iin4, Id4 and I04) of the current mode divider circuit is shown in eq.(5.26)

Iin4I04 Iout4 = (5.26) Id4

4 4 Let Iin4 = I0uu , Id4 = (I0uu + I0uKd) and I04 = I0GABAB , we obtain:

u4 Iout4 = 4 I0GABAB (5.27) u + Kd

5.4 Implementation of the postsynaptic transmission

In this section, the postsynaptic potential of each chemical synapse receptors will be presented with measured result. For the AMP A and NMDA receptors, the postsynap- 5.4. Implementation of the postsynaptic transmission 125 tic circuit of these two receptors employs the glutamate ISFET as the chemical input.

This input represents the change in the neurotransmitter concentration that is generated via the iontophoresis technique and is detected by the glutamate ISFET.

However, as stated earlier for the GABAA and GABAB receptors, an electrical signal from an AC current source will be used to simulate the detected neurotransmitter signal.

This is due to the diﬃculty in accessing the enzyme GABA-oxidase for the modiﬁca- tion of ISFETs to detect γ-aminobutyric acid. This enzyme has not been extracted for commercial use and there is only one publication that has reported on its extraction process [7].

The postsynaptic potential of a chemical synapse is given by eq.(5.28) where Vm is the postsynaptic potential, Cm is the equivalent membrane capacitance, INa is the current from the sodium channel, IK is the current from the potassium channel IL is the current from the leakage channel.

dVm Cm = −INa − IK − IL −Isyn (5.28) dt | {z } Hodgkin and Huxley model

From the electronic circuit point of view, eq.(5.28) can be illustrated as shown in

Fig.(5.13). The shaded area represents the Hodgkin and Huxley neuron circuit. Imple- mentation of INa and IK is based on the circuit realisation of Lazaridis et al. [8]. As the conductance gain of sodium and potassium channels are considerably higher than the synaptic or leakage conductance, the OTAs for Na and K current channels were implemented from DeWeerth et al. shown in Fig.(3.9). Isyn is referred to IAMP A, INMDA,

IGABAA and IGABAB . The simulation results of the bionics chemical synapse receptors 5.4. Implementation of the postsynaptic transmission 126 were obtained from the Cadence on the AMS C35B3C3 CMOS process.

I 4 syn I0nn + + E E syn - - K

3 Ileak I0mhm h

+ Vm +

Eleak E - - Na

Figure 5.13: Circuit of the bionics postsynaptic chemical synapse

5.4.1 Postsynaptic circuit for the AMP A receptor

The equations related to the postsynaptic potential of the AMP A receptor are shown in eq.(5.29).

dVmAMP A Cm = −INa − IK − IL − I dt AMP A (5.29)

IAMP A =g ¯AMP A rAMP A (Vm − EAMP A )

IAMP A in eq.(5.29) was implemented using the Bernoulli cell in Fig.(5.6) and the low transconductance gain OTA shown in Fig.(5.14). The relationship between the output current (Iout), the input diﬀerential voltage (Vin+ − Vin−) and the input bias current I01 Iin1 Iout1 40 4 Ib I 40 x1 4 40 40 4 4 Ib Iout Iin1  Id1 Irout C1 I01 100 4

Vb I Sig

Vin Vin

Electrode 1 5.4. Implementation of the postsynaptic transmission 127 - - - - ASig A - Micropipette V1 Sig V2 Iapply + + + ++ r (Ix1) is shown in eq.(5.30). The circuit analysis of this OTA can be viewed in section IoutSig Electrode 2 3.5.

PBS pH 7

1 (1 − )I (V − V )I x2 np x1 in+ in− Iout = (5.30) n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

where np is the subthreshold slope parameter of PMOS, nn is the subthreshold slope I I 02 03 parameter of NMOS and U is the thermal voltage. Iin2 Iout 2 T

5.6 5.6 5.6 5.6 I I d 2 d 3 I x1 c c 2 I02 3 I03 100 100 I 0.35 0.35 out

100 100 0.35 0.35 200 200 0.35 0.35

100 100 I I 0.35 0.35 I 02 0u in2 Iout 2 200 200 IrGABA B Vin 0.35 0.35 Vin

67.2 5.6

200 200 I 5.6 5.6 67.2 0.35 0.35 Iin2  Id 2 d 3 5.6 5.6 5.6 5.6 5.6 I 5.6 I02 C 0u 5.6 C2 3 200 200 0.35 0.35

I x2

Figure 5.14: Low transconductance gain OTA circuit

The output current (Iout−AMP A) of the Bernoulli cell shown in Fig.(5.6) was designated I in3 Iin3 Iin3 Iin3 Iout3 as the input bias current of the OTAI (Ix1) in Fig.(5.14). In this4 circuit conﬁguration, syn I0nn + + E E syn - - K

3 I03 Iin3 I03 Iin3 I03 Iin3 Ileak I0mhm h V + m + Eleak E - - Na

I I 04 in4 Iout 4

I d 4 ASig

I04 5.4. Implementation of the postsynaptic transmission 128 the output current of the OTA is:

(1 − 1 )I np 0AMP A Iout = · rAMP A(Vin+ − Vin−) (5.31) n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

Iout in eq.(5.31) and IAMP A in eq.(5.29) are comparable and it can be concluded that:

1 (1 − n )I0AMP A g¯ = p (5.32) AMP A n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

From the biological model of the AMP A receptor, the conductance gain of AMP A

(¯gAMP A ) is 0.1nS [4]. Thus, I0AMP A can be calculated based on this expression.

g¯ n (1 + nn + n2 )(n2 + n + 1)16U AMP A n np n n n T I0AMP A = (5.33) (1 − 1 ) np

With nn = 1.3, np = 1.28 and UT =25.82mV @ 300K, I0AMP A is 3.6nA. The amplitude of IdAMP A based on CAMP A = 1.5nF is 9.5nA.

The glutamate ISFET or the neurotransmitter sensor of this receptor couples the glutamate concentration change via the current-mode ISFET readout circuit of Shep- herd et al. [9]. The output current of this readout circuit represents IinAMP A or

CAMP A nUT [T ]αAMP A in eq.(5.14).

2γ −2VbAMP A nUT nUT CAMP A nUT [T ]αAMP A = Ibe e [ions] (5.34)

Rearranging eq.(5.34) yields: 5.4. Implementation of the postsynaptic transmission 129

! −nUT nCAMP A UT αAMP A VbAMP A = ln 2γ (5.35) 2 nU Ibe T

In eq.(5.34), both [T ] and [ions] represent the glutamate ions concentration at 1 mM.

Perturbation of the glutamate ions was carried out by using a micropipette ﬁlled with

1 M glutamate solution with all the parameters as described in section 4.5 of Chapter

4. The current source used for the glutamate injection was a Keithley 6221 AC current source. In this case, the current amplitude was -0.4µA (see more detail in chapter 4, section 4.5) and VbAMP A is 284.25mV. The overall circuit for the AMP A receptor is shown in Fig.(5.16). The measured and the simulation results are shown in Fig.(5.15).

When the current amplitude was set to a positive value, no response was observed.

1.336 -0.4uA Simulation 1.335 +0.4uA

1.334

1.333

1.332 Amplitude (V.)

1.331

1.330 0.2 0.4 0.6 0.8 time (s.)

Figure 5.15: Measured vs. simulation results for the AMP A receptor

5.4.2 Postsynaptic circuit for the NMDA receptor

The equations related to the postsynaptic potential of the NMDA receptor are shown in eq.(5.36). 3 10 3 10 3 5 3 10 t Na K i E E

h t t c r 4 n i

3 5 e h c e 3 3 5 r 3 3

10 10 4 r

m r d n e n u mh e o - - 0 0 3 + + 3 10 5 I I c Na K C

w m E E t n 3 e 10 i o 3 3 10 a 10 d u p t r 3 5

c o c e m r 3 e A i 10 h u i D f 3 3 3 5 C m c 5.4. Implementation of the postsynaptic transmission 130 10 i M

c 4 d m l N t

n n m 3 n n mh r p 10 - - 0 n 0 a + + V I I e t o r e m c c r d e i a i r 3 3 s u 10 10 3 3 f v 10 10 i u i n d 3 l 3 5 10 3 5 a C d m n p 3 A r 10 Na C K leak - - o + + 3 5 I E E T m c a s mGABA 04 I V n 3 10 a h 3 r 3 4 3 10 m 3 10 T 10 n

n mh 3 5 leak - - 0 0 + + e I I NMDA E leak - - 3 + + c I 10 E 3 5 n 3 10 a t r 3 m 10 c e 3 i C 10 u f u mNMDA i 0 A 3 d l I 10 V leak mAMPA d n p 3 5 E V Sig K

GABA A o r E m c o 3 a 10 f 3 s 10

3 3 t n 10 10 i a leak 3 u - - r 3 10 3 + + I 10 10 A 3 5 c T ) r 3 3 3 10 i 10 10 u GABA 3 V 0 10 2 c 01 I 03 ( 3 3

I 10 10 I 3 10 out B d A I i

Na 3 K A 10 o E E n leak 3 5 AMPA i B E m E a 3 10 A 3 g 3 5 10 i m G 3 h 10

3 3 S 5 3 o 10 3 3 10 3 r 10 4 10 m d n o

n mh f - - 0 0 + +

g I I 3 5 A A r o 3 3 3 5 10 10 e AMPA GABA L t GABA 3 1 01 1 l 10 3 I 10 d i C 3 NMDA I 10 f 01 m B 3 5 3 3 I 10 3 10 C 10 3 3 5 mGABA C 3 3 V 10 10 3 3 10

A 10 3 5 3 5 n 3 P 10 i

A 3 3 5 3 10 a d n M 3 D 3 10 i I 10 3 10 leak - - A m a + + I 3 5 M B

02 3 5 3 10 I r o AMPA N m AMPA 1 1

3 5 o d d C 3

o rGABA r f NMDA I 10 NMDA 1

I 3 1 10 g d o C d r B

f I 3 5 o

g A t 3 r A 10 L l B n o i i e leak B 3 10 f t 3 E 3 L a 10 l 10 A GABA i 3 f 10 E 3 3 5 inGABA m 10 G I

3 o 10

3 5 r d e o 3

3 10 10 f c

g 3 n 10 r 3 5 3 10 o 3 10 a e t 3 L t r 10 3 l 4 10 40 c i e 3 5 f 2 i 3 4 u 10 40 f C i d l A n n p

0 5 o A 4 3 n 40

10 m 0 c 3 10 5

4 a 40 t s

bAMPA e 2 t 3 5 u n V e d d u a o I d o r Figure 5.16: Full schematic of a Bionics chemical synapse for the AMPA receptor o d 3 o 10 T d a m

a e m t

r 3 4 e 4 10 bNMDA 40 t

40 n r V

n 4 T e 4 40 40 r T e E r r E F r u 3 10 F u S B C I S A C I n

0 5 A n

inGABA 0 5 I 4 100 4 100 5.4. Implementation of the postsynaptic transmission 131

dVmNMDA Cm = −INa − IK − IL − I dt NMDA (5.36) I = g B(V )r (V − E ) NMDA NMDA NMDA NMDA

INMDA in eq.(5.36) was implemented using the Bernoulli cell in Fig.(5.7), the sigmoid circuit in Fig.(5.8) and the low transconductance gain OTA in Fig.(5.14). The output current (Iout−Sig) of the combined circuits, shown in eq.(5.11), was designated as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit conﬁguration, the output current of the OTA is given by:

(1 − 1 )I np 0NMDA rNMDA Iout = n · (Vin+ − Vin−) (5.37) n (1 + n + n2 )(n2 + n + 1)16U ASig(−V2) n np n n n T 1 + exp { 2 } (np+np)UT

Iout in eq.(5.37) and INMDA in eq.(5.36) are comparable and it can be concluded that:

1 (1 − n )I0NMDA g¯ = p (5.38) NMDA n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T 1 1 2+ = (5.39) (−62V )[Mg ]o ASig(−V2) 1 + exp 1 + exp { 2 } 3.57mM (np+np)UT

From the biological model of the NMDA receptor, the conductance gain of NMDA

(¯gNMDA ) is 0.1nS [4]. Thus, I0NMDA is 3.6nA with nn = 1.3, np = 1.28 and UT =

25.82mV @ 300K. The value of IdNMDA based on CNMDA = 22nF is 4.84nA. ASig is 1.3 based on the assumption that the magnesium concentration is 1mM. The glutamate

ISFET or the neurotransmitter sensor of this receptor couples the glutamate concentration change via the current-mode ISFET readout circuit of Shepherd et al. [9]. The

output current of this readout circuit represents IinNMDA or CNMDA nUT [T ]αNMDA in eq.(5.9). 5.4. Implementation of the postsynaptic transmission 132

2γ −2VbNMDA nUT nUT CNMDA nUT [T ]αNMDA = Ibe e [ions] (5.40)

Rearranging eq.(5.40) yields:

! −nUT nCNMDA UT αNMDA VbNMDA = ln 2γ (5.41) 2 nU Ibe T

In eq.(5.40), [T ] and [ions] both represent the glutamate ions concentration at 1 mM.

Perturbation of the glutamate ions was carried out by using a micropipette ﬁlled with

1 M glutamate solution with all the parameters as described in section 4.5 of Chapter

4. The current source used for the glutamate injection was a Keithley 6221 AC current source. In this case, the current amplitude was -0.4µA and VbNMDA was 284.92mV. The overall circuit for the NMDA receptor is shown in Fig.(5.18). The measured and circuit simulation results are shown in Fig.(5.17). When the current amplitude was set to a positive value, no response was observed.

1.2898 -0.4uA Simulation +0.4uA 1.2896

1.2894 Amplitude (V.) 1.2892

1.2890 1 2 3 4 5 time (s.)

Figure 5.17: Measured vs. simulation results for the NMDA receptor 3 5.4. Implementation of the postsynaptic transmission 133 10 3 10 3 5 3 10 t Na K i E E

h t t c r 4 n i

3 5 e h c e 3 3 5 r 3 3

10 10 4 r

m r d n e n u mh e o - - 0 0 3 + + 3 10 5 I I c Na K C

w m E E t n 3 e 10 i o 3 3 10 a 10 d u p t r 3 5

c o c e m r 3 e A i 10 h u i D f 3 3 3 5 C m c 10 i M

c 4 d m l N t

n mh 3 5 leak - - 0 0 + + e I I NMDA E leak - - 3 + + c I 10 E 3 5 n 3 10 a t r 3 m 10 c e 3 i C 10 u f u mNMDA i 0 A 3 d l I 10 V leak mAMPA d n p 3 5 E V Sig K

GABA A o r E m c o 3 a 10 f 3 s 10

3 3 t n 10 10 i a leak 3 u - - r 3 10 3 + + I 10 10 A 3 5 c T ) r 3 3 3 10 i 10 10 u GABA 3 V 0 10 2 c 01 I 03 ( 3 3

I 10 10 I 3 10 out B d A I i

Na 3 K A 10 o E E n leak 3 5 AMPA i B E m E a 3 10 A 3 g 3 5 10 i m G 3 h 10

3 3 S 5 3 o 10 3 3 10 3 r 10 4 10 m d n o

n mh f - - 0 0 + +

g I I 3 5 A A r o 3 3 3 5 10 10 e AMPA GABA L t GABA 3 1 01 1 l 10 3 I 10 d i C 3 NMDA I 10 f 01 m B 3 5 3 3 I 10 3 10 C 10 3 3 5 mGABA C 3 3 V 10 10 3 3 10

A 10 3 5 3 5 n 3 P 10 i

A 3 3 5 3 10 a d n M 3 D 3 10 i I 10 3 10 leak - - A m a + + I 3 5 M B

02 3 5 3 10 I r o AMPA N m AMPA 1 1

3 5 o d d C 3

o rGABA r f NMDA I 10 NMDA 1

I 3 1 10 g d o C d r B

f I 3 5 o

g A t 3 r A 10 L l B n o i i e leak B 3 10 f t 3 E 3 L a 10 l 10 A GABA i 3 f 10 E 3 3 5 inGABA m 10 G I

3 o 10

3 5 r d e o 3

3 10 10 f c

g 3 n 10 r 3 5 3 10 o 3 10 a e t 3 L t r 10 3 l 4 10 40 c i e 3 5 f 2 i 3 4 u 10 40 f C i d l A n n p

0 5 o A 4 3 n 40

10 m 0 c 3 10 5

4 a 40 t s

bAMPA e 2 t 3 5 u n V e d Figure 5.18: Full schematic of a Bionics chemical synapse for the NMDA receptor d u a o I d o r o d 3 o 10 T d a m

a e m t

r 3 4 e 4 10 bNMDA 40 t

40 n r V

n 4 T e 4 40 40 r T e E r r E F r u 3 10 F u S B C I S A C I n

0 5 A n

inGABA 0 5 I 4 100 4 100 5.4. Implementation of the postsynaptic transmission 134

5.4.3 Postsynaptic circuit for the GABAA receptor

The equations related to the postsynaptic potential of the GABAA receptor are shown in eq.(5.42).

dVmGABAA Cm = −INa − IK − IL − I dt GABAA (5.42) I =g ¯ r (V − E ) GABAA GABAA GABAA m GABAA

IGABAA in eq.(5.42) was implemented using the Bernoulli cell in Fig.(5.8) and the low transconductance gain OTA in Fig.(5.14). The relationship between the output current

(Iout), the input diﬀerential voltage (Vin+ − Vin−) and the input bias current (Ix1) is shown in eq.(5.30).

The output current (Iout−GABAA ) of the Bernoulli cell shown in Fig.(5.8) was designated as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit conﬁguration, the output current of the OTA is:

(1 − 1 )I np 0GABAA Iout = · rGABA (Vin+ − Vin−) (5.43) n (1 + nn + n2 )(n2 + n + 1)16U A n np n n n T

I in eq.(5.43) and I in eq.(5.42) are comparable and it can be concluded that: out GABAA

1 (1 − n )I0GABAA g¯ = p (5.44) GABAA n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

From the biological model of the GABAA receptor, the conductance gain of GABAA (¯g ) is 0.1nS [4]. Thus, I can be calculated based on this expression: GABAA 0GABAA 5.4. Implementation of the postsynaptic transmission 135

g¯ n (1 + nn + n2 )(n2 + n + 1)16U GABAA n np n n n T I0GABA = (5.45) A (1 − 1 ) np

With nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, I01GABAA is 3.6nA. The value of

Id1GABAA based on C1GABAA = 820pF is 4.92nA, while the input pulse current IinGABAA has the maximum peak at 136.6nA. Fig.(5.20) shows the overall circuit of the GABAA receptor. The measured and simulation results are shown in Fig.(5.19).

1.315

1.314

1.313 Amplitude (V.) 1.312 Measured Simulation 1.311 0.1 0.2 0.3 0.4 0.5 0.6 time (s.)

Figure 5.19: Measured vs. simulation results for the GABAA receptor 40

4 50 nA 10 10 10 10 10 10 40 3 3 3 3 3 3 4 4 I0nn 40 40 V 50 nA 4 4 + mAMPA +

EAMPA E - - K 100 5 5 5 5 4 3 3 3 3

3 Ileak I0mhm h VbAMPA C 1AMPA I01AMPA + + Eleak E - - Na Id1AMPA

Cm 10 10 10 10 10 10 3 3 3 3 3 3 Current mode Log domain Transconductance ISFET readout filter for AMPA amplifier

40 10 10

4 50 nA 3 3 4 10 10 10 10 10 10 I n 40 0n 3 3 4 3 3 3 3 V 40 40 + mNMDA + 50 nA 4 4 E NMDA EK 10 10 - - 3 3 100 5 5 5 5 4 3 3 3 3 VmNMDA 10 10 I 3 ASig leak I0mhm h 3 3 V I bNMDA 01NMDA + + C Eleak E 1NMDA - - Na

10 10 Id1NMDA 10 10 10 3 3 Cm 3 3 3 10 10 10 3 3 3 Current mode 5.4. ImplementationLog domain of the postsynapticSigmoi transmissiond circuit for Transcond136uctance ISFET readout filter for NMDA B(V) amplifier

10 10 10 10 10 10 3 3 3 3 3 3 4 I0nn V + mGABAA + E GABA A EK 5 5 5 5 - - 3 3 3 3

IinGABA 3 A Ileak I0mhm h

C1GABA I + + A 01GABAA Eleak E - - Na I d1GABAA

Cm 10 10 10 10 10 10 3 3 3 3 3 3

Log domain Transconductance filter for GABAA amplifier

Figure 5.20: Full schematic of a Bionics chemical synapse for the GABAA receptor

10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 10 10 3 3 5.4.4 PostsynapticI circuit for theI GABAB receptor rGABAB out 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

I The equations related to the postsynaptic potential0u of the GABAA receptor are shown I02 10 10 10 10 I d 2 C2 C I in eq.(5.46). Id 3 3 3 3 3 3 inGABAB 10 10 10 I03 10 10 10 3 3 3 Current 3 3 3 mode 4th dV 10 10 10 10 power circuit 10 10 10 Log domain C mGABAB = −I 3− I − I − I 3 3 3 3 3 3 m dt Na K L GABAB filter for GABAB 4 (5.46) 10 u 10 I = g (V − E ) 10 10 3 GABA GABA 4 GABA 3 B B u + Kd B 3 3 4 I0nn

V 5 5 5 5 + mGABAB + I IGABA in eq.(5.46) was implemented using the BernoulliKd I0u cell in Fig.(5.10),04 the current E B E 3 3 3 3 GABA B - - K multiplication circuit in Fig.(5.11), the current divider circuit in Fig.(5.12) and the low 10 10 3 10 10 Ileak I0mhm h 3 3 transconductance gain OTA in Fig.(5.14). The relationship3 between the3 output10 current10 10 + + 3 3 3 E (Iout), theleak input diﬀerential voltage (VinE+Na − Vin−) and the input bias current (Ix1) is Transconductance - - Current mode

amplifier shown in eq.(5.30). Cm divider circuit 5.4. Implementation of the postsynaptic transmission 137

u4 The output current (Iout4) of the circuit that implements 4 shown in Fig.(5.12) u +Kd was designated as the input bias current of the OTA (Ix1) in Fig.(5.14). In this circuit conﬁguration, the output current of the OTA is given by:

(1 − 1 )I 4 np 0GABAB u Iout = · (Vin+ − Vin−) (5.47) n (1 + nn + n2 )(n2 + n + 1)16U u4 + K n np n n n T d

I in eq.(5.47) and I in eq.(5.46) was comparable and it can be concluded that: out GABAB

1 (1 − n )I0GABAB g¯ = p (5.48) GABAB n (1 + nn + n2 )(n2 + n + 1)16U n np n n n T

From the biological model of the GABAB receptor, the conductance gain of GABAB (¯g ) is 0.1nS [4]. Thus, I can be calculated based on this expression: GABAB 0GABAB

g¯ n (1 + nn + n2 )(n2 + n + 1)16U GABAB n np n n n T I0GABA = (5.49) B (1 − 1 ) np

With nn = 1.3, np = 1.28 and UT = 25.82mV @ 300K, I0GABAB is 3.6nA. Based on C1

= 22nF, C2 = 10nF, Id1 = 1nA, Id2 = 11nA, I01 = 33nA, I0u = 10nA and I0uKd = 1nA, while the input pulse current IinGABAB has the maximum peak at 381nA. Fig.(5.22) shows the overall circuit of the GABAB receptor. The measured and simulation results are shown in Fig.(5.21).

A microphotograph of the fabricated chemical synapse integrated circuit is shown in

Fig.(5.23). The chip area of the four chemical synapses is 1120 × 1120 µm2. The total power dissipation of all the circuits in this chip is 168.3µW from a 3.3V supply. The printed circuit board used in this thesis was designed in Orcad version 15.1. Dimension of this board is 350mm x 350mm. A photograph of this PCB is shown in Fig.(5.24). 5.4. Implementation of the postsynaptic transmission 138

1.3200 Simulation Measured 1.3195

1.3190

1.3185

Amplitude (V.) 1.3180

1.3175

1.3170 1 2 3 4 5 time (s.)

Figure 5.21: Measured vs. simulation results for the GABAB receptor

The overall experimental setup is shown in Fig.(5.25). The closed up illustration of the tip of the micropipette and the glutamate ISFET is shown in Fig.(5.26).

Figure 5.23: Microphotograph of the fabricated chemical synapse 5.4. Implementation of the postsynaptic transmission 139 3 10 3 10 3 5 3 10 t Na K i E E

h t t c r 4 n i

3 5 e h c e 3 3 5 r 3 3

10 10 4 r m r d n e n u mh e o - - 0 0 3 + + 3 10 5 I I c Na K C

w m E E t n 3 e 10 i o 3 3 10 a 10 d u p t r 3 5

c o c e m r 3 e A i 10 h u i D f 3 3 3 5 C m c 10 i M

c 4 d m l N t

n n m 3 n n mh r p 10 - - 0 n 0 a + + V I I e t o r e m c c r d e i a i r 3 3 s u 10 10 3 3 f v 10 10 i u i n d receptor 3 l 3 5 10 3 5 a C d m n p 3 A r 10 Na C K leak - - o B + + 3 5 I E E T m c a s mGABA 04 I V n 3 10 a h 3 r 3 4 3 10 m 3 10 T 10 n n mh GABA 3 5 leak - - 0 0 + + e I I NMDA E leak - - 3 + + c I 10 E 3 5 n 3 10 a t r 3 m 10 c e 3 i C 10 u f u mNMDA i 0 A 3 d l I 10 V leak mAMPA d n p 3 5 E V Sig K

GABA A o r E m c o 3 a 10 f 3 s 10

3 3 t n 10 10 i a leak 3 u - - r 3 10 3 + + I 10 10 A 3 5 c T ) r 3 3 3 10 i 10 10 u GABA 3 V 0 10 2 c 01 I 03 ( 3 3

I 10 10 I 3 10 out B d A I i

Na 3 K A 10 o E E n leak 3 5 AMPA i B E m E a 3 10 A 3 g 3 5 10 i m G 3 h 10

3 3 S 5 3 o 10 3 3 10 3 r 10 4 10 m d n o

n mh f - - 0 0 + +

g I I 3 5 A A r o 3 3 3 5 10 10 e AMPA GABA L t GABA 3 1 01 1 l 10 3 I 10 d i C 3 NMDA I 10 f 01 m B 3 5 3 3 I 10 3 10 C 10 3 3 5 mGABA C 3 3 V 10 10 3 3 10

A 10 3 5 3 5 n 3 P 10 i

A 3 3 5 3 10 a d n M 3 D 3 10 i I 10 3 10 leak - - A m a + + I 3 5 M B

02 3 5 3 10 I r o AMPA N m AMPA 1 1

3 5 o d d C 3 o rGABA r f NMDA I 10 NMDA 1

I 3 1 10 g d o C d r B f I 3 5 o e g A t 3 r A 10 L l B n o i i e leak B 3 10 f t 3 E 3 L a 10 l 10 A GABA i 3 f 10 E 3 3 5 inGABA m 10 G I

3 o 10

3 5 r d e o 3

3 10 10 f c

g 3 n 10 r 3 5 3 10 o 3 10 a e t 3 L t r 10 3 l 4 10 40 c i e 3 5 f 2 i 3 4 u 10 40 f C i d l A n n p

Figure 5.22: Full schematic of a Bionics chemical synapse for the 0 5 o A 4 3 n 40

10 m 0 c 3 10 5

4 a 40 t s bAMPA e 2 t 3 5 u n V e d d u a o I d o r o d 3 o 10 T d a m a e m t r 3 4 e 4 10 bNMDA 40 t

40 n r V n 4 T e 4 40 40 r T e E r r E F r u 3 10 F u S B C I S A C I n

0 5 A n

inGABA 0 5 I 4 100 4 100 5.4. Implementation of the postsynaptic transmission 140

Figure 5.24: The photograph of bionics chemical synapse chip test and application board

Figure 5.25: Experimental setup for bionics chemical synapse chip 5.4. Implementation of the postsynaptic transmission 141

Figure 5.26: Closed up picture of the glutamate ISFET and the tip of the micropipette

The parameters for the implementation of each receptor are summarised in Table (5.1).

Table 5.1: AMP A, NMDA, GABAA and GABAB parameters hhhh hhhh Receptor(x) hhh AMP A NMDA GABAA GABAB Parameter hhhh Ex(mV) 1070 1070 970 970 ENa(mV) 1115 1115 1115 1115 EK (mV) 988 988 988 988 Eleak(mV) 989.3 989.3 989.3 989.3 Ileak(nA) 34 34 34 34 Cm(pF) 3.14 3.14 3.14 3.14

The measured results of the postsynaptic circuit in AMP A, NMDA, GABAA and

GABAB in Fig.(5.15), (5.17), (5.19) and (5.21) respectively, have a noisy reading. As the output of the OTA has a high output impedance, the thermal noise at this node was high and it also had a tendency to pick up the 50Hz line signal. One possible solution to reduce this noise is to introduce of a metallic case to shield the test board. 5.5. Summary 142

The experiment from Jakobson et al. [10] concluded that the intrinsic MOSFET noise dominates the noise characteristic of ISFETs. The drain current noise spectra of ISFETs

(SID) operated in weak inversion region [10] is shown in eq.(5.50)

2 4 Cinv q Not 2 1 SID = 4 · 2 ID (5.50) (Cox + CD) (kT ) WL f

where Cinv, Cox and CD are the inversion, oxide and depletion capacitance per area,

Not is the eﬀective oxide traps density per unit area, k is the Boltzmann’s constant, T is the absolute temperature, W and L are the width and length of the MOSFET, ID is the DC drain current and f is the frequency bandwidth. From eq.(5.14), noise on the

ISFETs can be minimised if the gate area (WL) is maximised.

Also, the measured results did not match perfectly with the simulation results. One of possible reasons is that there is a temperature diﬀerence between the simulation and test bench environment. This is because weak inversion circuits are temperature dependent

(i.e. thermal voltage term, UT ). From Fig.(5.15) and (5.19), it can be observed that there is diﬀerence time delay between simulation and measured results. This delay is due to a parasitic body source capacitance of the input MOSFETs.

5.5 Summary

In this chapter, the ﬁrst bio-inspired chemical synapse with glutamate ISFETs as the chemical front-end on silicon integrated circuit has been presented. Based on the chemical synapse model of Destexhe, the AMP A and NMDA receptors were fully implemented with glutamate ISFETs in analogue current-mode subthreshold CMOS. The measured results of the electro-physiological characteristics of these receptors match 5.5. Summary 143 well with their models in circuit simulation. With this bio-inspired chemical synapse integrated circuit, a complete CMOS chemical synapse for the receptors GABAA and

GABAB will be readily achieved with the introduction of a γ-aminobutyric acid (GABA) sensor [7] for the GABAA and GABAB receptors.

The chemical synapse implementation accomplished in this work has the potential to create the artiﬁcial receptors of the chemical synapse. These synthetic receptors can be used as a neural link or neural bridge to bypass damaged or terminated neural signal path. This will be possible, in the future, when the ISFET and the processing circuit are integrated onto the same chip. Another challenge is to match the ISFET’s sensing area to the synapse of a pre-synaptic neuron to detect the actual neurotransmitter emitted. References

[1] C. Toumazou, J. Georgiou, and E. M. Drakakis, “Current-mode analogue cir-

cuit representation of hodgkin and huxley neuron equations,” Electronics Letters,

vol. 34, no. 14, pp. 1376–1377, 1998.

[2] T. W. Berger, A. Ahuja, S. H. Courellis, S. A. Deadwyler, G. Erinjippurath, G. A.

Gerhardt, G. Gholmieh, J. J. Granacki, R. Hampson, M. C. Hsaio, J. Lacoss, V. Z.

Marmarelis, P. Nasiatka, V. Srinivasan, D. Song, A. R. Tanguay, and J. Wills,

“Restoring lost cognitive function,” Engineering in Medicine and Biology Magazine,

IEEE, vol. 24, no. 5, pp. 30–44, 2005.

[3] P. Georgiou and C. Toumazou, “A silicon pancreatic beta cell for diabetes,”

Biomedical Circuits and Systems, IEEE Transactions on, vol. 1, no. 1, pp. 39–

49, 2007.

[4] Z. F. M. Alain Destexhe and T. J. Sejnowski, Kinetic models of synaptic transmis-

sion., 2nd ed., ser. Methods in Neuronal Modeling (2nd ed.). Cambridge, MA:

MIT Press, 1998, pp. 1–26.

[5] R. F. Lyon and C. Mead, “An analog electronic cochlea,” Acoustics, Speech and

Signal Processing, IEEE Transactions on, vol. 36, no. 7, pp. 1119–1134, 1988.

144 REFERENCES 145

[6] R. A. Kaul, N. I. Syed, and P. Fromherz, “Neuron-semiconductor chip with chemical

synapse between identiﬁed neurons,” Phys. Rev. Lett., vol. 92, p. 038102, Jan

2004. [Online]. Available: http://link.aps.org/doi/10.1103/PhysRevLett.92.038102

[7] A. Yamamura, Y. Kimura, S. Tamai, and K. Matsumoto, “Gamma-aminobutyric

acid (gaba) sensor using gaba oxidase from penicillium sp. kait-m-117,” ECS Meet-

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[8] E. Lazaridis and E. M. Drakakis, “Full analogue electronic realisation of the

hodgkin-huxley neuronal dynamics in weak-inversion cmos,” in Engineering in

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[10] C. G. Jakobson and Y. Nemirovsky, “1/f noise in ion sensitive ﬁeld eﬀect transistors

from subthreshold to saturation,” Electron Devices, IEEE Transactions on, vol. 46,

no. 1, pp. 259–261, 1999. Chapter 6

Conclusion and Future Work

A silicon chemical synapse implemented using subthreshold CMOS circuits and enzyme- modified ISFETs as neurotransmitter sensors was implemented in this thesis. This implementation is the very first artificial synapse with the ability to sense a neurotransmitter (glutamate). The significance of this work is that it can be further developed into a new prosthetic tool to reconnect breaks in the neural pathway, due to damaged or deteriorated nervous cells. To create this artificial synapse, a sub-nano-Siemens operational transconductance amplifier with a bulk driven input and the double differential pairs techniques was introduced. Typical ISFETs were modified with glutamate oxidase (GluOX) and merged with current-mode ISFET readout circuits to produce linear glutamate concentration sensors. Furthermore, the mathematical models for Destexhe’s chemical synapse was realised and formulated in weakly inverted CMOS circuits.

146 6.1. Contribution 147

6.1 Contribution

The concept of applying electronic circuits to bio-inspired systems was introduced in

Chapter 2. Initially, the principles of the neuron communication system were presented, such as the physical characteristics of neurons, the presence and function of the ion channels, the generation of the action potential and the diﬀerent mathematical models of the action or membrane potential. Additionally, the fundamentals of synapses and the chemical synapse mathematical model were described. Furthermore, diﬀerent types of neuro-inspired circuits, both synapse and neuron, were also reviewed.

Chapter 3 began by laying out the speciﬁcation of the operational transconductance ampliﬁer (OTA) that is required for this application, a transconductance gain in the sub-nano Siemens range with a nano-Ampere range bias current. According to the Des- texhe’s chemical synapse model, the minimum conductance of each receptor is 0.1nS.

This requirement was fulﬁlled by a novel OTA that combines several OTA design techniques, which are: the bulk driven MOSFET and the drain current normalisation.

Circuit analysis of the OTA topologies was described in detail, from the elementary diﬀerential pair to the novel technique that combined the bulk driven with drain current normalisation. Circuit simulation conﬁrmed that this new OTA design was able to acquire a transconductance gain of 0.1nS with a 3.6nA bias current.

In this work, the ISFET was used as the coupler between the electronics and the chemical world. The enzyme immobilised ISFET functioned as the neurotransmitter sensor for the bionics chemical synapse. Also, the principle of the ISFET and the ion perturbation technique called iontophoresis were introduced and explained in Chapter 4.

Firstly, the physical details and the chemical sensitivity of the ISFET were outlined. 6.1. Contribution 148

Secondly, the procedure carried out to immobilise the ISFET with the glutamate oxidase enzyme was given. Furthermore, a current-mode ISFET readout circuit (H-cell) in [1] was adopted to achieve a linear relationship between the ion concentration and output, compared to the non-linear voltage-mode readout circuit in [2]. This glutamate ISFET and H-cell were combined to create the ﬁrst linear and sensitivity-tunable glutamate sensor. Lastly, the iontophoresis technique used for generating the fast ion

flow was shown and the validity of this method was confirmed via an experiment. This experiment was considered as the first iontophoresis technique to generate and verify a millisecond H+ perturbation on the ISFET.

The integration of the glutamate ISFET with the current-mode CMOS circuits formed the bionics chemical synapse as shown in Chapter 5. The log domain ﬁlter, the sigmoid diﬀerential pair, the sub-nano Siemens OTA and the translinear circuit, all operated in the weak inversion region were designed to perform the mathematical model of the

Destexhe’s chemical synapse. The iontophoresis technique was employed as the vir- tual glutamate neurotransmitter release. Full implementation of the chemical synapse receptors was carried out for the AMP A and NMDA receptors. These artiﬁcial chemical synapses can be considered as a novel bionics chemical synapse implementation which has an actual chemical input. The measured results from a fabricated chip and the simulation results of the artiﬁcial chemical synapse exhibit good matching in the post-synaptic response.

All publications related to this thesis can be found in Appendix A at the end of this thesis. 6.2. Recommendation for Future Work 149

6.2 Recommendation for Future Work

Future developments according to the contents in this thesis are proposed in the following areas:

6.2.1 Integration of the components on the same chip

For practical use in the future, this CMOS chemical synapse should be amended to have all the discrete component, such as the capacitors and the ISFETs, integrated onto the same chip.

The capacitor value being used currently in the Bernoulli cell of each receptor is in the order of nano Farads. This range of capacitance occupies an area of about one millimetre square on silicon. As the capacitance is linearly proportional to the chip area, reduc- tions in the magnitude of the bias currents, for instance: IdAMP A in eq.(5.3), IdNMDA in eq.(5.7), IdGABAA in eq.(5.14), Id1 in eq.(5.19) and Id2 in eq.(5.23)) are examples of ways to economise the chip area. Another possibility is to employ circuit techniques such as the active capacitor multiplier [3], to enlarge the small-on-chip capacitance.

The ISFETs that function as the neurotransmitter sensors of the CMOS chemical synapse circuit should also be integrated onto the same chip as the processing circuit. The unmodiﬁed CMOS ISFET has been pioneered since 2000 [4]. The same chip integration of the chemical sensors and the electronic circuits will lead to a potentially implantable or in-vivo nerve bridge in the future. 6.2. Recommendation for Future Work 150

6.2.2 The non-invasive and direct extracellular glutamate detector

Measurements of extracellular neurotransmitter is vital for neurologists to understand more about neuron physiology and behaviour. Glutamate is one of the neurotransmitter that have been studied via extracellular measurements because of its role in some functions of the brain [5] and in Alzheimer’s disease [6]. Two methods have been pioneered for this measurement: the microdialysis technique [7] and the visual optical method [8].

For extracellular glutamate measurement under the microdialysis technique, a penetra- tion of the neuron is required. Also, this technique has limitations in rapid and local concentration detection [8]. The optical technique on extracellular can measure local concentration for each individual cell of neurons. However, it is an indirect measurement of glutamate concentration and requires an optical tool to interpret the ﬁnal result.

The linear current-mode readout circuit and the glutamate ISFET can be combined and used as an electronic extracellular glutamate sensor. The ability of ISFETs as a real time and fast chemical sensor has been proven [9]. This tool can be considered as a non-invasive and real-time extracellular glutamate detector which could be potentially used to record glutamate activity on synapses to understand complex brain processes, or even learning and memory mechanisms.

6.2.3 Live neuron experiment

As the ultimate objective of this work is to pave the way for the development of a medical treatment that will be able to re-connect broken neural signal path from damaged nerve cells, therefore an experiment on this bionics chemical synapse with a live neurons should 6.2. Recommendation for Future Work 151 be carried out as the ﬁrst step towards this goal. This requires the cooperation of the biologists who are capable of performing experiments with cell cultures. An example of an experiment on the chemical synapse in cell cultures was demonstrated in Kaul’s

PhD work [10]. In his work, two types of synapse cells, exhibitatory and inhibitatory, were extracted from a snail (Lymnaea stagnalis). These extracted neuron cells can be used as the live neuron interface with the bionics chemical synapse. References

[1] L. M. Shepherd and C. Toumazou, “A biochemical translinear principle with weak

inversion ISFETs,” Circuits and Systems I: Regular Papers, IEEE Transactions

on, vol. 52, no. 12, pp. 2614–2619, 2005.

[2] H. Nakajima, M. Esashi, and T. Matsuo, “The pH response of organic gate ISFETs

and the inﬂuence of macro-molecule adsorption,” Nippon Kagaku Kaishi, vol. 10,

pp. 1499–1508, 1980.

[3] G. A. Rincon-Mora, “Active capacitor multiplier in miller-compensated circuits,”

Solid-State Circuits, IEEE Journal of, vol. 35, no. 1, pp. 26–32, 2000.

[4] B. Palan, K. Roubik, M. Husak, and B. Courtois, “CMOS ISFET-based structures

for biomedical applications,” in Microtechnologies in Medicine and Biology, 1st

Annual International, Conference On. 2000, 2000, pp. 502–506.

[5] W. McEntee and T. Crook, “Glutamate: its role in learning, memory,

and the aging brain,” Psychopharmacology, vol. 111, pp. 391–401, 1993,

10.1007/BF02253527. [Online]. Available: http://dx.doi.org/10.1007/BF02253527

[6] M. R. Hynd, H. L. Scott, and P. R. Dodd, “Glutamate-mediated excitotoxicity and

neurodegeneration in alzheimers disease,” Neurochemistry international, vol. 45,

no. 5, pp. 583–595, 10 2004.

152 REFERENCES 153

[7] s. Fallgren and R. Paulsen, “A microdialysis study in rat brain of dihydrokainate,

a glutamate uptake inhibitor,” Neurochemical Research, vol. 21, pp. 19–25, 1996,

10.1007/BF02527667. [Online]. Available: http://dx.doi.org/10.1007/BF02527667

[8] S. Okumoto, L. L. Looger, K. D. Micheva, R. J. Reimer, S. J. Smith, and W. B.

Frommer, “Detection of glutamate release from neurons by genetically encoded

surface-displayed fret nanosensors,” Proceedings of the National Academy of Sci-

ences of the United States of America, vol. 102, no. 24, pp. 8740–8745, June 14

2005.

[9] S. Thanapitak, P. Pookaiyaudom, P. Seelanan, F. J. Lidgey, K. Hayatleh, and

C. Toumazou, “Veriﬁcation of ISFET response time for millisecond range ion stim-

ulus using electronic technique,” Electronics Letters, vol. 47, no. 10, pp. 586–588,

2011.

[10] R. Kaul, “Chemical synapses on semiconductor chips,” Ph.D. dissertation, Tech-

nischen Universitat Munchen, 2007. Appendix A

Publications

Journal Papers

S. Thanapitak and C. Toumazou, “Bionic chemical synapse,” under revision for

Biomedical Circuits and Systems, IEEE Transactions on

Electronics Letters

S. Thanapitak, P. Pookaiyaudom, P. Seelanan, F. J. Lidgey, K. Hayatleh, and

C. Toumazou, “Veriﬁcation of isfet response time for millisecond range ion stim-

ulus using electronic technique,” Electronics Letters, vol. 47, no. 10, pp. 586–588,

2011.

Conference Papers

S. Thanapitak and C. Toumazou, “Towards a bionic chemical synapse,” in Circuits

and Systems, 2009. ISCAS 2009. IEEE International Symposium on, 2009, pp.

677–680.

154 Appendix B

PCB outline of Bionics Chemical Synapse

155 156 D C B A GND

U39 C_820pF

1 + 1 1 2 STIM_GABAA OUT_GABAA_PRE I_BETA I_10nA IN_T_2 IB2_1 OUT_T_2 BIAS_T_2 GND 28 27 26 25 24 23 22 21 20 19 18 2 2 NC3 GND GND I_10nA IN_T_2 I_BETA

OUT_T_2

BIAS_T_2 C_GABAA

IT_10nA_2

VB NC2

VB 929

OUT_GABAA 17 STIM_GABAA

S_COMMON STIM_NMDA

030 16 STIM_NMDA

U38 C_22nF

ELEAK DA C_NM + -

ELEAK 131 15 1 2

ECH OUT_NMDA

ECH

232 14 OUT_NMDA_PRE

80D800 D1 5

333 13 5 80D800

40D400 D2 4

434 12 4 40D400

20D200 SOURCE 3

535 11 3

20D200 ISFET_44_CHIP

3 3

10D100 V_GA 2

636 10 2 10D100

VDD V_GB 1

737 9 1 3.3V_CHIP

OUT_GABAB OUT_AMPA

OUT_GABAB_PRE

838 8 OUT_AMPA_PRE U101 5_CON

C NC4 C_AMPA + -

939 7 1 2 GND U40 STIM_GABAB C2_GABAB C1_GABAB ILEAK I0 IN_T_1 IT_10nA_1 OUT_T_1 BIAS_T_1 STIM_AMPA NC1 C_1.5nF 1 2 3 4 5 6 40 41 42 43 44 4 4 I0 IB2_1 IN_T_1 ILEAK Figure B.1: PCB schematic for a bionics chemical synapse chip OUT_T_1 BIAS_T_1 STIM_AMPA STIM_GABAB

U41 C_22nF

+ -

1 2 5 5

U42 C_10nF

+ -

1 2 GND D C B A 157 D C B A GND GND GND OUT_GABAA OUT_GABAB 1 1 2 3 4 5 2 3 4 5

BNC SMA

6 6 2 3 4 5 2 3 4 5 2C2 C2

8 8

BC3

BC3 G1 G2 G3 G4 G1 G2 G3 G4

5 U77 5 U78

BC1

BC1 G1 G2 G3 G4 G1 G2 G3 G4 1 1 OUT OUT

BNC BNC

VCC VCC GND GND SIG SIG

7 4 7 4 SIG SIG 1 1 U17 U36 1 1 U14 U15 GND GND + - + - +6V +6V LMC6001 LMC6001 3 2 3 2 2 2 1 ECH U73 Test_Point OUT_GABAA_PRE OUT_GABAB_PRE GND OUT_GABAA_PRE OUT_GABAB_PRE OUT_AMPA OUT_NMDA GND GND

6 6

2C2 C2

8 Buffer of output signal 8

BC3 BC3

U75 U76 5 BNC SMA

2 3 4 5 2 3 4 5

BC1 BC1

1 1 Unbuffer of output signal 2 3 4 5 2 3 4 5 OUT OUT

G1 G2 G3 G4 G1 G2 G3 G4

VCC GND VCC 3 GND 3

G1 G2 G3 G4 G1 G2 G3 G4 7 4 7 4 BNC BNC SIG SIG SIG SIG GND GND 1 1 + - + - +6V +6V LMC6001 LMC6001 U18 U19 1 1 U37 U99 3 2 3 2 1 VB U74 Test_Point OUT_AMPA_PRE GND GND GND GND GND OUT_AMPA_PRE OUT_NMDA_PRE OUT_NMDA_PRE 4 4 SMA 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 Figure B.2: PCB schematic for the OPAMP buﬀer and BNC, SMA ports G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 BNC BNC BNC BNC BNC SIG SIG SIG SIG SIG SIG SIG SIG SIG SIG SMA SMA SMA SMA 1 1 1 1 1 1 1 1 1 1 U2 U3 U6 U7 U8 U9 U10 U11 U12 U13 5 5 ELEAK 1 1 1 1 1 U68 Test_Point U69 Test_Point U70 Test_Point U71 Test_Point U72 Test_Point OUT_AMPA OUT_NMDA OUT_GABAB OUT_GABAA D C B A 158 D C B A GND GND GND 1 1 BNC BNC 2 3 4 5 2 3 4 5 2 3 4 5 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 BNC BNC BNC BNC 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 SIG SIG SIG SMA 1 1 1 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 U100 U58 U4 SIG SIG SIG SIG 1 1 1 1 U62 U63 U64 U65 1 2 2 2 2 2 2 Test_Point SIG2 SIG2 SIG2 SIG2 U114 1 1 Test_Point U110 Test_Point U111 Ib1 (Bias current for ref. elec) BIAS_T_1 BIAS_T_2 SIG1 SIG1 SIG1 SIG1 1 1 1 1 U43U44 JUMPER2 U45 JUMPER2 U46 JUMPER2 JUMPER2 GND GND GND D100 D200 D400 D800 3 3 BNC SMA BNC SMA 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 BNC BNC 2 3 4 5 2 3 4 5 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 SIG SIG SIG SIG SIG SIG 1 1 1 1 U47 U48 U49 U50 1 1 U66 U67 Ib2 (10nA bias) VREF to source of ISFET and BIAS_T 1 1 IB2_1 IB2_1 4 4 1 1 Test_Point U109 Test_Point U108 IN_T_2 Figure B.3: PCB schematic for the BNC, SMA ports I IN_T_1 Test_Point Test_Point GND GND GND GND U102 U107 BNC SMA BNC SMA BNC SMA BNC SMA IN_T1,2 to drain of ISFET 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 SIG SIG SIG SIG SIG SIG SIG SIG 1 1 1 1 1 1 1 1 U20 U21 U24 U25 U22 U23 U34 U35 5 5 Current sink (positive value) I0 1 1 1 1 ILEAK I_10nA Test_Point U103 Test_Point U104 Test_Point U105 Test_Point U106 I_BETA D C B A 159 D C B A GND GND GND GND 1 1 BNC SMA BNC SMA BNC SMA BNC SMA 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 G1 G2 G3 G4 SIG SIG SIG SIG SIG SIG SIG SIG Current source (negative value) 1 1 1 1 1 1 1 1 U26 U27 U28 U29 U30 U31 U32 U33 STIM signal from gen. 1 1 1 1 U57 U61 U59 U60 SIG1 SIG1 SIG1 SIG1 2 2 SIG_GABAB SIG_AMPA SIG_NMDA SIG2 SIG2 SIG2 SIG2 SIG_GABAA 2 2 2 2 JUMPER2 JUMPER2 JUMPER2 JUMPER2 STIM_AMPA STIM_NMDA STIM_GABAA STIM_GABAB JUMPER to select current source for STIM channel 3 3 STIM_AMPA STIM_NMDA STIM_GABAA STIM_GABAB 2 2 2 2 GND SIG2 SIG2 SIG2 SIG2 SIG1 SIG1 SIG1 SIG1 4 4 1 1 1 1 Figure B.4: PCB schematic for the BNC, SMA ports II U51U52 JUMPER2 U53 JUMPER2 U54 JUMPER2 JUMPER2 2 3 4 5 2 3 4 5 SELECT FOR EACH INDIVIDUAL SYNAPSE# G1 G2 G3 G4 G1 G2 G3 G4 BNC BNC SIG SIG 1 1 U5 U16 2 2 SIG2 SIG2 TAP OUT_T for char SIG1 SIG1 5 5 1 1 OUT_T_2 U55U56 JUMPER2 JUMPER2 OUT_T_1 OUTPUT from TRANS4 OUT_T_2 OUT_T_1 D C B A