Open Ahmed I Almatar SHC Thesis SP21.Pdf

THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF MECHANCIAL ENGINEERING

Biomolecular Materials Emulate Short-Term Synaptic Plasticity for Signal Processing

AHMED ALMATAR SPRING 2021

A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Mechanical Engineering with honors in Mechanical Engineering

Reviewed and approved* by the following:

Joseph Najem Assistant Professor of Mechanical Engineering Thesis Supervisor

Jean-Michel Mongeau Assistant Professor of Mechanical Engineering Honors Adviser

* Electronic approvals are on file. i

ABSTRACT

The amount of digital data we are producing is increasing at rapid rates and might soon exceed our current capacity to process it, mostly due to energy limitations. Therefore, to process this vast amount of data, optimizing computing per unit energy is key. Currently, neuromorphic computing, a model for computing that borrows key computational aspects of the human brain, is a leading solution to optimizing computing. However, despite major progress, solid-state neuromorphic computing hardware bears little resemblance to biological neurons and synapses, and thus, is still lagging in performance and energy efficiency compared to the brain.

This study investigates a different approach to neuromorphic systems by using biomolecular memory elements as opposed to silicon-based elements. Replacing silicon with biomembranes to form memristors enables the emulation of short-term synaptic plasticity—a signal filtering property of biological synapses. The performance of the biomembrane has been tested experimentally using a DC signal as the input and a solid-state neuron circuit as the load.

The biomembrane was determined to be exhibiting short-term synaptic plasticity by controlling the firing rate of the neuron. The results suggest that our biomolecular memristor is capable of performing basic signal processing tasks, namely, high-pass filtering. However, the behavior of networks of biomembranes remains unknown and is of great interest.

TABLE OF CONTENTS

LIST OF FIGURES ...... iii

LIST OF TABLES ...... iv

ACKNOWLEDGEMENTS ...... v

Chapter 1 Introduction ...... 1

Chapter 2 Background ...... 3

2.1 Smart Materials ...... 3 2.2 Mimicking the Nervous System ...... 4 2.3 Constructing a Biomembrane Synapse Mimic ...... 5 2.4 Neuron Circuity ...... 7 2.5 Research goals ...... 9

Chapter 3 Methods ...... 10

3.1.1 Droplet Interface Bilayer/Biomembrane Formation ...... 10 3.1. 1 Droplet Interface Bilayer/Biomembrane Model ...... 12 3.2.1 Integrate and Fire Neuron Model ...... 12 3.2.2 Integrate and Fire Neuron Simulation ...... 16 3.2.2 Integrate and Fire Neuron Assembly ...... 18

Chapter 4 Results ...... 19

4.1 Simulation Results ...... 19 4.2 Experimental Results ...... 22

Chapter 5 Discussion ...... 27

5.1 Short-term Synaptic Plasticity and Firing Frequency ...... 27 5.2 Alamethicin Peptides ...... 28 5.3 Computing Abilities ...... 28

Chapter 6 Conclusion and Future Work ...... 29

BIBLIOGRAPHY ...... 30

iii

LIST OF FIGURES

Figure 1: A schematic describing the assembled synapse-neuron model. The biomolecular synapse receives a signal and processes it using short-term synaptic plasticity. The processed signal controls the firing frequency of the neuron model, emulating neural sensitization. 2

Figure 2: The presynaptic voltage spikes represent stimuli entering the synapse. The sensitized and adapted voltage spikes are shown above it [7]...... 4

Figure 3: (A) biological neuron composition (B) artificial neuron composition [13]...... 5

Figure 4: An artificial synapse constructed using biomembranes. Two droplets of water with a lipid layer in an oil solution. The lipid layer contains proteins and protein-like chemicals [5].6

Figure 6: Each of circuit branches with a resistor is an analog to the three currents types of currents discovered by Hodgkin and Huxley. The variable resistors are the equivalent to ion channels opening and closing (which is how the neuron moves action potentials). The constant resistor represents “leakage”, which is the permissibility of the neuron walls. Each branch has a voltage source equivalent to potentials related to their operation (when they are open and closed for channels). Lastly the capacitor represents the neuron’s membrane ability to hold charge [18]...... 8

Figure 7: Snapshot of the assembled and formed DIB with thickness in the order of 100-101 nm. 11

Figure 8: The three main components to make an integrate and fire neuron. The capacitor is an analog to the neuro’s charge accumulating capacity, the resistor is an analog to the neuron’s ion channels, and the comparator is an analog to the threshold voltage needed to fire the neuron signal...... 13

Figure 9: An analog integrate-and-fire neuron with a specified threshold voltage VThr. The circuit is divided into 4 parts: input, integrating amplifier, comparator, and output [14]...... 14

Figure 10: Final desgin of the neuron circuitry. The design parameters are identical to Table 1. The input part of the circuit here is a constant current (dc voltage + resistance), but it will be the DIB current for the model...... 16

Figure 11: The neuron voltage vs time for constant current input. This is the result of the simulation run for the circuit in Figure 10...... 17

Figure 12: The neuron circuit assembly...... 18

Figure 13: Simulated DIB current vs time for an input voltage of 145 mV...... 20

Figure 14: Simulated Neuron voltage vs time for an input voltage of 145 mV...... 20 iv

Figure 15: Simulated DIB current vs time for an input voltage of 150 mV...... 21

Figure 16: Simulated Neuron voltage vs time for an input voltage of 150 mV...... 21

Figure 17: Experimental DIB current vs time with no alamethicin peptides present...... 22

Figure 18: Experimental Neuron voltage vs time for an input voltage of 160 mV...... 23

Figure 19: Experimental DIB current vs time for an input voltage of 145 mV...... 24

Figure 20: Experimental firing frequency of the neuron vs time for an input voltage of 145 mV. 24

Figure 21:Experimental DIB current vs time for an input voltage of 150 mV...... 25

Figure 22: Experimental firing frequency of the neuron vs time for an input voltage of 150 mV. 25

Figure 23: Experimental DIB current vs time for an input voltage of 160 mV...... 26

Figure 24: Experimental firing frequency of the neuron vs time for an input voltage of 160 mV. 26

Figure 25: A hypothesized model for the parallel biomembrane synapses network. The droplets are connected in parallel...... 29 v

LIST OF TABLES

Table 1: The desgin parameters for this project’s integrate and fire neuron...... 15

Table 2: The DIB model parameters...... 19

ACKNOWLEDGEMENTS

I would like to thank Dr. Joseph Najem for believing in me early on, and for investing time in teaching me many skills over the last 18 months. I am extremely appreciative for his continued support and guidance. Next, I would like to thank Dr. Najem’s PhD student, Ahmed

Mohamed, for helping me with parts of the modeling and the data collection—this work would not have been possible without him. I also want to thank Dr. Mongeau, my honors advisor, for taking the time to guide me through the thesis process.

Lastly, I want to acknowledge my family. I am deeply grateful for the opportunity to attend Penn State. My parents (Ibrahim Almatar and Rasha Alsannonh) are the reason I was able to attend such a great institution. I will forever be grateful for them and everything they did for me.

Chapter 1

Introduction

Traditional computing methods are energy expensive and are only growing in their energy cost. It is estimated that the amount of digital data generated by the year 2040 will exceed the amount of energy humans are expected to produce to process this data [1]. One approach to resolving this problem is neuromorphic computing, a model for energy efficient computing that uses key functional aspects of the human brain and nervous system [2]. The human brain is very efficient in processing information while consuming no more than 20 Watts of power [1].

Despite significant advancements, solid-state neuromorphic systems still fall short in terms of power efficacy and neural density compared to the human brain [3]. Many of the downsides of solid-state neuromorphic systems come from the limitations of three-terminal transistors and the materials used to make them. An alternative approach to solid-state neuromorphic systems is memory-resistive elements which are two-terminal electrical elements relating electrical charge and magnetic flux [4]. Memristors systems can collocate information processing and memory as well as perform computations by emulating biological synapses and neurons.

Rather than mimicking the behavior of biological systems via solid-state devices, using biological components that have the same mechanism as synapses and neurons is a more efficient path to neuromorphic computing. Biological or biomolecular memristors are neuromorphic devices consisting of synthetic lipid bilayers embedded with voltage-activated ion channels [3]. Biomolecular memristors resolve some of the main issues with solid-state 2 neuromorphic devices, namely, efficiency and functional density. Biomolecular memristors directly mimic biological synapse, hence they are able to capture the same mechanism governing the synaptic functionality. More importantly, there is evidence to them exhibiting short-term synaptic plasticity [3]. Short-term synaptic plasticity (STP) is the ability for synapses to dynamically change their conductance depending on previous inputs, meaning that exhibiting

STP enables memristors to act as synapses [5].

This project involves assembling a network of a biomolecular memristor and a solid-state analog neuron to show that biomolecular memristor is able to mimic biological synapses, specifically, short-term plasticity in biological synapses. The assembled synapse-neuron model should exhibit neural facilitation. Figure1 is a summary of the final assembly.

Chapter 2

Background

2.1 Smart Materials

Smart materials are systems that can respond to stimuli in their environment and are capable of performing sensing, control, and actuation tasks; they are a primitive analog of a biological body [6][7]. Examples of common smart materials are piezoelectric ceramics or electroactive polymers [8]. Current smart materials have limitations, such as requiring high currents and voltages to function, and being large in size [3].

Biomimicry is the process of imitating models of nature to solve problems. Bio-mimics have the potential to be used as replacements for the traditional fabrication methods used in making smart materials[9]. One candidate for biomimicry in the case of smart materials is the nervous system. The nervous system is responsible for processing stimuli in the body’s environment. The central processing unit of the nervous system is the brain, while elementary, yet impactful, processing occurs in the neurons, specifically at the synapse level.

The synapse is a fundamental element of the nervous system that is responsible for connecting nerve cells. The synapse consists of a small gap across which impulses pass by diffusion of a neurotransmitter, which is a chemical that carries information. A fundamental property of synapses is short-term synaptic plasticity (STP), which allows the synapses to perform an array of computational tasks. For instance, synapses can dynamically filter sensory input leading to adaptation and sensitization in vital organs such as the retina [10] [11][12][5].

Figure 2 illustrates the adaptation and sensitization process in biological synapses. 4

Figure 2: The presynaptic voltage spikes represent stimuli entering the synapse. The sensitized and adapted voltage spikes are shown above it [7]. 2.2 Mimicking the Nervous System

The nervous system combines sensing, signal processing, computing, and communication, tasks current smart materials consume large amounts of energy to perform. The low energy consumption of the nervous system makes it an excellent candidate to designing future smart materials that can process and compute.

Considering the sense of touching, for example, the skin is the receptor that senses objects, which is the change in pressure. That creates a signal known as an action potential that travels through the neurons and axons. Synapses, which are located at the ends of neurons, carry the actional potential to the next neuron.

Compared to that is an artificial system that consists of: a signal, voltage changes, voltage pulses, an artificial synapse/bio-mimics, and post-synaptic current [13].

Figure 3 is a schematic comparing biological systems to artificial systems. 5

Figure 2: (A) biological neuron composition (B) artificial neuron composition [1] Figure 3: (A) biological neuron composition (B) artificial neuron composition [13]

2.3 Constructing a Biomembrane Synapse Mimic

Mimicking the overall functionality and structure of the nervous system is the working principle of neuromorphic systems. Neuromorphic systems rely on very-large-scale-integration circuits (VLSI) to mimic biological neurons’ functionalities. However, VLSI circuits are energy consuming and large in size compared to a biological neuron [3].

Biological or biomolecular memristors are neuromorphic devices made using synthetic biological elements [3]. Biomolecular memristors resolve some of the main issues with solid- state neuromorphic devices, energy efficiency and size. The main component of such memristors is the biological membranes, which are selective permeable barriers within living organisms. The biomembranes are configured in such a way to mimic a biological synapse, meaning that 6 biomembranes will exhibit short-term synaptic plasticity. Therefore the biomembranes will be able to filter signals as needed, in the same manner as biological synapses [5].

In order to create biomembranes, a droplet interface bilayer (DIB), which is a layer formed between aqueous phase droplets needs to be formed. The aqueous phase droplets have a protein-doped lipid layer in between [3].

Figure 4 shows an example of the synapse created using the DIB.

Figure 4: An artificial synapse constructed using biomembranes. Two droplets of water with a lipid layer in an oil solution. The lipid layer contains proteins and protein-like chemicals [5].

Biomembranes are analogues to memory circuit elements, memelements, used in solid- state neuromorphic systems. Memelements are a type of passive circuit elements that can also act as memories. There are two types of memelements, memristive elements and memcapacitive elements[3][14][15][16]. The behavior of memelements can be described by the following equation [15]: 7 y(t) = g(퐱, u, t) u(t) and d퐱/dt = f(퐱, u, t) (1)

where x is a state variable vector and 푢(푡)is either the current or the voltage across the element.

푔 and 푓 are continuous vector functions that depend on physical parameters of the memelemnts.

One example of the physical parameters that define the output of memelemnts is the areal concentration of the proteins and the biomembrane area [14][3].

2.4 Neuron Circuity

Neurons are the major unit forming the nervous system. Sensory neurons are the neurons responsible for sensing stimuli changes in the environment. The stimuli are then converted to an electrical signal known as an action potential. The action potential is transmitted to the next neuron through the synapse at the end of the previous neuron. Figure 5 is a simplification of the process of starting the action potential from a stimulus.

Figure 5: The sensory neuron receives the signal from stimuli in the environment, if the stimuli is large enough, the action potential (AP) is going to start traveling down the neuron. (image credit: Scott Clarke, Monash University). 8 Models of neurons go back to the 20th century, when Hodgkin and Huxley performed experiments on the giant axon of the squid and found three different types of ion current [17].

Figure 6 shows the circuit equivalent of the model proposed by Hodgkin and Huxley [18].

More recent models of the neuron make use of similar components and techniques. However, analog models tend to feature a switching mechanism in place of the variable resistances

[19][14][20].

9 2.5 Research goals

This research project aims to design, assemble, and study biomolecular neural networks capable of emulating sensory computing in biological neurons. A single computing unit will consist of a silicon-based neuron connected via membrane-based, a biomolecular synapse. The assembled units will be used to investigate the ultimate role of short-term plasticity in the nervous system.

Figure 1 demonstrates the planned model to be tested. By understanding the role of short-term plasticity in the nervous system, new smart materials that have lower energy consumption can be developed.

10 Chapter 3

Methods

In this chapter I will discuss the experimental setup used which is consisting of two main parts. The first part being the DIB/biomembrane synaptic mimic, and the second being the integrate-and-fire neuron circuitry.

3.1.1 Droplet Interface Bilayer/Biomembrane Formation

The DIB is acting as the synapse, processing signals, and the response is going to be a function of the peptides and lipids used to form the DIB [21]. For the purpose of showing short- term synaptic plasticity, a biological memristor should be sufficient [22]. In this experiment, the suitable lipid to use is DPhPC. DPhPC is a lipid containing diphytanoyl fatty acid chains that have been used to produce stable planar lipid membranes [23].

To get DPhPC in the lab, lyophilized powder form is used. Lyophilized powder is a freeze-dried powder form of the DPhPC lipid. A portion of DPhPC is dissolved in a solution of chloroform in a glass vial. The solution is gently stirred and mixed under a steady stream of dry nitrogen until the chloroform evaporates and a lipid film forms at the bottom of the vial. The vial containing the lipid film is left in a vacuum for 12 hours to make sure all of the chloroform has been evaporated. A buffer solution of sodium chloride is added to the lipid film before freezing the hydrated film. The final step is extruding the (lipid) liposomes needed for the DIB formation using a commercial extruder of a suitable size [21].

11 To form the biomembrane, a type of peptide protein is added to the lipid (liposome) mixture. In this experiment, alamethicin was used in concentration in the range of 0.5-1 M. To prepare the alamethicin solution, dry alamethicin is dissolved in ethanol alcohol. The DIB is assembled on thin sliver wire electrodes placed inside an oil reservoir. Figure 7 shows a snapshot of the assembled and formed DIB on the sliver wire electrodes.

Figure 7: Snapshot of the assembled and formed DIB with thickness in the order of 100-101 nm.

12 3.1. 1 Droplet Interface Bilayer/Biomembrane Model

The DIB model is a system of differential equations that relates the parameters of the biomembrane memristor together. The current of a DIB follows the equation[3]:

퐼퐷퐼퐵 = 퐺(푁푎, 퐴푚 ) 푉푖푛 (2) where 푉푖푛 is the voltage applied the membrane and G is the conductance of the membrane which is a function of the membrane area 퐴푚 and the number of alamethicin pores per unit area 푁푎.

The state equations describing and the number of alamethicin pores per unit area 푁푎 is:

푉 푑푁푎 1 ( ⁄ 푉푒) = 푉 (푁0푒 − 푁푎) (3) 푑푡 ( ⁄푉 ) τ0푒 푡 where 푁0, τ0 , 푉푡, and 푉푒 are constant properties of the membrane and alamethicin.

The state equation describing the membrane area 퐴푚 is:

푑퐴푚 1 2 = (훼푉푖푛 − 퐴푚(푡)) (4) 푑푡 τ푒푤 where τ푒푤 and 훼 are constant properties of the membrane.

The conductance 퐺(푁푎, 퐴푚 ) is defined as:

퐺푚 = 퐺푢푁푎(퐴0(1 + 퐴푚)) (5) where 퐺푢 is the nominal conductance.

3.2.1 Integrate and Fire Neuron Model

The analog integrate-and-fire neuron is used in the assembly as an indicator of the biomembrane’s ability to mimic synaptic plasticity. The neuron circuit needed should be sensitive enough to record small changes in the current it will receive from the DIB. Therefore, the neuron circuit must have some ability to amplify the current it receives from the DIB. 13 More importantly than amplification, the neuron must have 3 main components. A

capacitive component, a resistive component, and a comparator component. The capacitive part

is needed in order to mimic the neuron’s ability to integrate the signal by accumulating charge,

the resistive part is needed to emulate the ion channels letting the ions move inside and outside

the neuron, and lastly, the comparator is used as a trigger to when the neuron reaches the

threshold potential for firing a signal. Figure 8 visually describes the three main components.

The original Hodgkin and Huxley model of an integrate and fire neuron incorporated a

version of the mentioned elements. However, instead of using a comparator to trigger threshold

potential, their model made use of three variable resistance to trigger the firing of the neuron

[24]. Using a variable resistance is only feasible for a theoretical/simulation version of the 14 model, not an analog circuit [25]. The analog circuit needed must have a threshold trigger in order for the neuron to fire.

The Hodgkin and Huxley model needs modifications to be implemented as an analog circuit. Such modifications are found in the literature. Weiss [14] shows the basic circuit structure discussed earlier in Figure 8. The circuit is divided up into 4 parts, input, integrating, comparing to the threshold, and firing. Figure 9 shows the circuit details as shown in [14].

Figure 9: An analog integrate-and-fire neuron with a specified threshold voltage VThr. The circuit is divided into 4 parts: input, integrating amplifier, comparator, and output [14].

The input part receives current from the biomembrane in the order of nanoamps to milliamps. The current is connected to the source end of a P-type transistor. The P-type transistor serves as a switch stopping the current from flowing when it is not desired. Following the input part, the signal flows into an integrating amplifier that achieves two goals, integrate the current

(add-up charge) and amplify the signal from the order of 10-9 – 10-3 (Amps) to the order of 100 -

101 (Volts). The third part of the circuit is the comparator Op-Amp. The comparator compares the amplified integrated signal to the desired threshold voltage for firing. When the comparator 15 fires high, the N-type transistor connected to the integrating amplifier opens and lets the built-up voltage discharge as the spike voltage signal. Simultaneously, the P-type input transistor switches off to stop current from flowing into the integrating amplifier.

The circuit shown in [14] is a good candidate for the purposes of this project. The parameters of the design selected for this project are in Table 1.

Table 1: The desgin parameters for this project’s integrate and fire neuron.

Parameter Description

P-type Transistor ALD1117

N-type Transistor ALD1116

RReset 150k Ω

CMem 1000nF

CReset 500nF

U1 & U2 LT1793

Rout 6.8k Ω

VThr -2.5 V

VDD +10 V

VSS -10 V

16 3.2.2 Integrate and Fire Neuron Simulation

To simulate the neuron model to approaches were used, SPICE based LTSPICE and

MALAB’s Simulink. The neuron’s input is eventually going to be the current obtained from the

DIB simulation. However, to test the neuron DC current can be used. Figure 10 shows the final circuit model in LTSPICE.

17 To test the model, an input of DC current is used as a test. Figure 11 shows the output for the input of the neuron for 1휇퐴 input.

Figure 11: The neuron voltage vs time for constant current input. This is the result of the simulation run for the circuit in Figure 10.

2The graph shows the neuron firing with constant intervals, the behavior expected for constant current.

18 3.2.2 Integrate and Fire Neuron Assembly

The analog neuron assembly was completed as the circuit in Figure 11. Figure 12 shows the final breadboard assembly.

Figure 12: The neuron circuit assembly. 19 Chapter 4

Results

4.1 Simulation Results

The simulation produced two different types of data: The DIB/Biomembrane current

(IDIB), and the neuron voltage (VN). The neuron voltage is then used to calculate the frequency of firing as a function of time. This is achieved by calculating the off-time of the neuron voltage spikes.

The following were the constant values used for the constants required for the DIB current model. The constants assume a 1 휇푀 concentration of alamethicin.

Table 2: The DIB model parameters.

Parameter Value

-5 푁0 4.7×10

푉푒 0.004 V

푉푡 0.025 V

-3 τ0 100×10 s

τ푒푤 1.5 s

푚2 훼 14.4 푉2

1 퐺푢 5×10-9 Ω

-8 2 퐴0 3.8×10 m

Running the test 3 times with two different voltages applied to the DIB (145 mV, 150 mV). The following are current vs time and neuron voltage vs time plots for the two runs. 20 For a 145 mV input, the DIB and the neuron responses are shown in Figures 13 and 14:

Figure 13: Simulated DIB current vs time for an input voltage of 145 mV.

Figure 14: Simulated Neuron voltage vs time for an input voltage of 145 mV.

For a 145 mV input, the DIB and the neuron responses are shown in Figures 15 and 16:

Figure 15: Simulated DIB current vs time for an input voltage of 150 mV.

Figure 16: Simulated Neuron voltage vs time for an input voltage of 150 mV.

22 4.2 Experimental Results

This section will present the experimental data. The data was denoised using MATLAB filters. The experiment produced three different types of data: The DIB/Biomembrane current

(IDIB), the DIB/Biomembrane voltage (VDIB), and the neuron voltage (VN). The neuron voltage is then used to calculate the frequency of firing as a function of time. This is achieved by calculating the off-time of the neuron voltage spikes.

The first test conducted was with a DIB not doped with alamethicin peptides. Figure 17 shows the current vs time for the DIB without alamethicin:

Figure 17: Experimental DIB current vs time with no alamethicin peptides present.

Adding alamethicin with the concentration of 0.5 휇푀, and running the test 3 times with three different voltages applied to the DIB (145 mV, 150 mV, 160 mV). The following are current vs time and frequency of firing vs time plots for the three runs. Firing frequency is, 푓 =

1 , where 푡표푓푓 is the time in between spikes of the neuron voltage, and 푡표푛 is the time each 푡표푓푓+푡표푛 spike takes to conclude. Figure 18 is a sample of the neuron voltage:

Figure 18: Experimental Neuron voltage vs time for an input voltage of 160 mV.

24 For a 145 mV input, the DIB and the neuron responses are shown in Figures 19 and 20:

Figure 19: Experimental DIB current vs time for an input voltage of 145 mV.

Figure 20: Experimental firing frequency of the neuron vs time for an input voltage of 145 mV.

For a 150 mV input, the DIB and the neuron responses are shown in Figures 21 and 22:

Figure 21:Experimental DIB current vs time for an input voltage of 150 mV.

Figure 22: Experimental firing frequency of the neuron vs time for an input voltage of 150 mV.

For a 160 mV input, the DIB and the neuron responses are shown in Figures 23 and 24:

Figure 23: Experimental DIB current vs time for an input voltage of 160 mV.

Figure 24: Experimental firing frequency of the neuron vs time for an input voltage of 160 mV. 27 Chapter 5

Discussion

5.1 Short-term Synaptic Plasticity and Firing Frequency

The alamethicin-doped, biomembrane memristor exhibited short-term synaptic plasticity.

This is evident by the increase of the firing frequency for constant input voltage, meaning a decrease in time between voltage spikes. This phenomenon is known as facilitation, a function of short-term plasticity. Facilitation is increased synaptic strength for repeated stimuli, meaning increased importance of the stimulus as it is repeated [26]. In this case, DC voltage input was applied to the biomembrane memristor, which is equivalent to a stimulus being repeated.

The biomembrane memristor increased its output current for the same input voltage over a period of time. Meaning that the biomembrane was able to change its conductance as time passed. The three different input voltages lead to different steady-state currents, the higher the voltage, the higher the steady-state current is. Higher steady-state currents lead to higher firing frequencies (less time between spikes). This was true for both the simulation and experimental tests.

The firing frequency closely followed the current behavior, as the current increased, the frequency increased. This is to be expected since the solid-state neuron integrator circuit fires as a function of the capacitor charge.

28 5.2 Alamethicin Peptides

Alamethicin pores enable the DIB to leak current and therefore change the DIB’s conductance as the number of open pores changes. This is evident in figure 18, where the DIB was formed with no alamethicin peptides. The result was a current too small to make the solid- state neuron fire or register a current above level of noise (it was less than the order of 10-12

Amps). Compared to the simulation, which is performed using double the concentration of alamethicin than the experiment, the steady-state current value differed between the two concentrations by a significant margin.

Other peptides might give a different reaction than that of alamethicin and will have different voltage and current levels. Different peptides might also experience different functions of short-term plasticity other than facilitation.

5.3 Computing Abilities

Since the biomembrane exhibited short-term plasticity, specifically facilitation, there is enough evidence that it is acting as a memristor. Memristors, as discussed earlier, are the basis of neuromorphic computing. Biomembrane synaptic mimics offer an energy efficient, and compact sized neuromorphic computing compared to silicon-based counterparts.

29 Chapter 6

Conclusion and Future Work

In this study, we discovered that peptide doped biomembranes experience short-term synaptic plasticity. Short-term plasticity implies that the biomembranes are behaving as memristors. The experimental data showed evidence of facilitation, a property of short-term plasticity. The synaptic mimics can be used in neuromorphic computing applications to perform basic signal processing and computing.

The literature on networks of biomembrane synaptic mimics is still developing. The behavior of networks of biomembranes in parallel is not well investigated up to this date. A future area of investigation is the role such networks can play in neuromorphic systems. Figure

25 shows a hypothesized model for the parallel biomembrane synapses networks.

Figure 25: A hypothesized model for the parallel biomembrane synapses network. The droplets are connected in parallel.

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ACADEMIC VITA Ahmed Almatar [email protected] EDUCATION The Pennsylvania State University––University Park, PA Graduation: May 2021 Bachelor of Science in Mechanical Engineering, Honors Honors & Awards: KAUST Gifted Students Program, Schreyer Honors College EXPERIENCEs Penn State Mechanical Engineering, Undergraduate Research Assistant November 2019 – May 2021 • Worked on an undergraduate thesis with the topic of bio-inspired smart materials. • Modeled the behavior of an integrate-and-fire neuron using analog circuit components. • Created an experimental setup to test the viability of using bio-membrane/analog neuron systems to perfume basic competing tasks such as filtering signals. Virginia Tech Center for Energy Harvesting, Undergraduate Research Assistant June 2019 - August 2019 • Spent two months in Dr.Lei Zuo’s lab working on wave energy harvesting. • Studied the Viability of Saline Water Desalination Using Hybrid Renewable Energy. • Presented the findings in forms of a formal and a poster presentation. Penn State Fluid Transport Laboratory, Undergraduate Research Assistant June 2018 – August 2019 • Spent two months in Dr. Rui Ni’s Lab working on interfacial mass transfer in turbulent flow. • Helped in making an experimental design’s electrical system. • Managed buying equipment that cost $5000. Penn State College of Engineering Advising Center, Lead Peer Advisor August 2019 – May 2021 • Gave peers technical help & non-technical advice related to college life & class selection, etc. • Improved the advisees flow into the advising center by reducing walk-ins waiting times (around 4 advisees/hour). Penn State Eberly College of Science, Undergraduate Learning Assistant January 2018 – January 2020 • Led help sessions for science courses, and organized reviews for the exams. • Held office hours for 2 hours a week in cooperation with other undergraduate learning assistants. SKILLS • Software: MATLAB, Simulink/Simscape, LTspice, PSpice. • Languages: Bilingual (English and Arabic) LEADERSHIP AND INVOLVEMENT Penn State Circle K, Fundraising Chair/VP August 2019 – March 2021 • Have been an active service member since August 2019. • Responsible for organizing fundraising events of the club. • Fundraised $150 - $ 200 per semester. Penn State Alternative Breaks, Baltimore, MD & Atlanta, GA, Volunteer March 2019 & November 2018 • Volunteered on two week-long that focused on refuges rights and access to education (60+ hours) Leadership Institute, State College, PA, Participant May 2019 • a week-long leadership camp focused on developing transferable skills, social justice, and active citizen.