DOCSLIB.ORG

Open Ahmed I Almatar SHC Thesis SP21.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. ii 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 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). ........................................................................ 7 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
Load more

Recommended publications
  • A Neural Network Framework for Cognitive Bias
    fpsyg-09-01561 August 31, 2018 Time: 17:34 # 1 HYPOTHESIS AND THEORY published: 03 September 2018 doi: 10.3389/fpsyg.2018.01561 A Neural Network Framework for Cognitive Bias Johan E. Korteling*, Anne-Marie Brouwer and Alexander Toet* TNO Human Factors, Soesterberg, Netherlands Human decision-making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. To substantiate our viewpoint, Edited by: we discern and explain four basic neural network principles: (1) Association, (2) Eldad Yechiam, Technion – Israel Institute Compatibility, (3) Retainment, and (4) Focus. These principles are inherent to (all) neural of Technology, Israel networks which were originally optimized to perform concrete biological, perceptual, Reviewed by: and motor functions. They form the basis for our inclinations to associate and combine Amos Schurr, (unrelated) information, to prioritize information that is compatible with our present Ben-Gurion University of the Negev, Israel state (such as knowledge, opinions, and expectations), to retain given information Edward J.
    [Show full text]
  • Injury Establishes Constitutive Μ-Opioid Receptor Activity Leading to Lasting Endogenous Analgesia and Dependence
    University of Kentucky UKnowledge Theses and Dissertations--Physiology Physiology 2013 INJURY ESTABLISHES CONSTITUTIVE µ-OPIOID RECEPTOR ACTIVITY LEADING TO LASTING ENDOGENOUS ANALGESIA AND DEPENDENCE Gregory F. Corder University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Corder, Gregory F., "INJURY ESTABLISHES CONSTITUTIVE µ-OPIOID RECEPTOR ACTIVITY LEADING TO LASTING ENDOGENOUS ANALGESIA AND DEPENDENCE" (2013). Theses and Dissertations--Physiology. 10. https://uknowledge.uky.edu/physiology_etds/10 This Doctoral Dissertation is brought to you for free and open access by the Physiology at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Physiology by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained and attached hereto needed written permission statements(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine). I hereby grant to The University of Kentucky and its agents the non-exclusive license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known. I agree that the document mentioned above may be made available immediately for worldwide access unless a preapproved embargo applies.
    [Show full text]
  • 196296475.Pdf
    SCIENCE ADVANCES | RESEARCH ARTICLE ENGINEERING Copyright © 2018 The Authors, some rights reserved; Stretchable organic optoelectronic exclusive licensee American Association sensorimotor synapse for the Advancement Yeongjun Lee1,2,3*, Jin Young Oh3,4*, Wentao Xu1,5,6, Onnuri Kim7, Taeho Roy Kim8, of Science. No claim to 3 9 3 3 7 original U.S. Government Jiheong Kang , Yeongin Kim , Donghee Son , Jeffery B.-H. Tok , Moon Jeong Park , Works. Distributed 3† 1,2,10† Zhenan Bao , Tae-Woo Lee under a Creative Commons Attribution Emulation of human sensory and motor functions becomes a core technology in bioinspired electronics for next- NonCommercial generation electronic prosthetics and neurologically inspired robotics. An electronic synapse functionalized with License 4.0 (CC BY-NC). an artificial sensory receptor and an artificial motor unit can be a fundamental element of bioinspired soft elec- tronics. Here, we report an organic optoelectronic sensorimotor synapse that uses an organic optoelectronic synapse and a neuromuscular system based on a stretchable organic nanowire synaptic transistor (s-ONWST). The voltage pulses of a self-powered photodetector triggered by optical signals drive the s-ONWST, and resultant informative synaptic outputs are used not only for optical wireless communication of human-machine interfaces but also for light-interactive actuation of an artificial muscle actuator in the same way that a biological muscle fiber contracts. Downloaded from Our organic optoelectronic sensorimotor synapse suggests a promising strategy toward developing bioinspired soft electronics, neurologically inspired robotics, and electronic prostheses. INTRODUCTION Light cognition is an important sensory function for bioinspired Our human body performs not only myriads of sensing functions electronics (12–15), for example, an artificial visualization system.
    [Show full text]
  • Neural Control of Human Movement
    97818_ch19.qxd 8/4/09 4:16 PM Page 376 CHAPTER 19 Neural Control of Human Movement CHAPTER OBJECTIVES ➤ Draw the major structural components of the ➤ Outline motor unit facilitation and inhibition and brain, including the four lobes of the cerebral the contribution of each to exercise performance cortex and responsiveness to resistance training ➤ Discuss specific pyramidal and extrapyramidal ➤ Discuss variations in twitch characteristics, resist- tract functions ance to fatigue, and tension development in the different motor unit categories ➤ Diagram the anterior motor neuron and discuss its role in human movement ➤ Describe mechanisms that adjust force of muscle action along the continuum from slight to ➤ Draw and label the basic components of a maximum reflex arc ➤ Define fatigue and discuss factors that act and ➤ Define the terms (1) motor unit, (2) neuromuscular interact to induce neuromuscular fatigue junction, and (3) autonomic nervous system ➤ List and describe functions of the proprioceptors ➤ Summarize the events in motor unit excitation within joints, muscles, and tendons prior to muscle action 376 97818_ch19.qxd 8/4/09 4:16 PM Page 377 CHAPTER 19 Neural Control of Human Movement 377 The effective application of force during complex learned Brainstem movements (e.g., tennis serve, shot put, golf swing) depends on a series of coordinated neuromuscular patterns, not just on The medulla, pons, and midbrain compose the brain- muscle strength. The neural circuitry in the brain, spinal cord, stem. The medulla, located immediately above the spinal and periphery functions somewhat similar to a sophisticated cord, extends into the pons and serves as a bridge between computer network.
    [Show full text]
  • Robotic Induction of Neuromodulation Via Paired Brain Stimulation with Mechanical Stimulation
    ROBOTIC INDUCTION OF NEUROMODULATION VIA PAIRED BRAIN STIMULATION WITH MECHANICAL STIMULATION A Dissertation Presented to The Academic Faculty By Euisun Kim In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology May 2020 Copyright c Euisun Kim 2020 ROBOTIC INDUCTION OF NEUROMODULATION VIA PAIRED BRAIN STIMULATION WITH MECHANICAL STIMULATION Approved by: Dr. Jun Ueda, Advisor George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Dr. Minoru Shinohara School of Biological Sciences Dr. Frank Hammond Georgia Institute of Technology George W. Woodruff School of Mechanical Engineering Dr. Dobromir Rahnev Georgia Institute of Technology School of Psychology Georgia Institute of Technology Dr. YongTae (Tony) Kim George W. Woodruff School of Date Approved: December 5, 2019 Mechanical Engineering Georgia Institute of Technology Soli Deo Gloria ACKNOWLEDGEMENTS I would like to first thank my advisor, Dr. Jun Ueda, for his dedicated guidance through- out my graduate career. His patience and generosity helped me go through my PhD study for the last five and a half years. His excellence in research and passion for learning new topics have inspired me to become a professional researcher. Also, his sincere care for his students encouraged me to dream to become a warm-hearted teacher. It is my great honor to have had him as my academic advisor and mentor. I would like to express my sincere thanks to my reading committee, Dr. Minoru Shino- hara, Dr. YongTae (Tony) Kim, Dr. Frank Hammond, and Dr. Dobromir Rahnev. I truly appreciate all helpful suggestions that they have given to me, with regard to both on my research, and on my career.
    [Show full text]
  • The Glutamate Hypothesis of Depression: the Effect of Stress and Glucocorticoids on Glutamate Synapse and the Action of Antidepressants
    International PhD Program in Neuropharmacology XXV Cycle THE GLUTAMATE HYPOTHESIS OF DEPRESSION: THE EFFECT OF STRESS AND GLUCOCORTICOIDS ON GLUTAMATE SYNAPSE AND THE ACTION OF ANTIDEPRESSANTS Doctorate thesis Giulia Treccani Coordinator: Prof. Filippo Drago Tutor: Prof. Maurizio Popoli UNIVERSITY OF CATANIA Acknowledgements I would like to thank many people, without whom my work would not be possible: Prof. Maurizio Popoli, my tutor, who wisely supervised my work and gave me trust during the PhD program. Prof. Filippo Drago who gave me the opportunity to take part to this PhD program that represented an important moment for my scientific growth. Dr. Laura Musazzi whose experience, advices, supervision and help have been extremely important to me. Prof. Giorgio Racagni, the Head of the Department of Pharmacological and Biomolecular Sciences, University of Milano. Drs. Daniela Tardito, Alessandra Mallei, Alessandro Ieraci, Mara Seguini, with whom I worked during the PhD program. Dr. Carla Perego from University of Milano for all immunofluorescence and total internal reflection fluorescence microscopy experiments. Prof. Giambattista Bonanno and Dr. Marco Milanese from University of Genova, for all glutamate and GABA release experiments. Prof. Antonio Malgaroli and Dr. Jacopo Lamanna from Scientific Institute San Raffaele, in Milano, for all the experiments of electrophysiology. Prof. Maria Pia Abbracchio for her kind support after my graduation. Special thanks to Ella, Sandro, Marta, Carola, Davide, Chiara and Jan. 2 INDEX Abstract ..............................................................................
    [Show full text]
  • Micro-, Meso- and Macro-Dynamics of the Brain
    Research and Perspectives in Neurosciences György Buzsáki Yves Christen Editors Micro-, Meso- and Macro-Dynamics of the Brain Research and Perspectives in Neurosciences More information about this series at http://www.springer.com/series/2357 Gyorgy€ Buzsa´ki • Yves Christen Editors Micro-, Meso- and Macro-Dynamics of the Brain Editors Gyorgy€ Buzsa´ki Yves Christen The Neuroscience Institute Fondation Ipsen New York University, School Boulogne-Billancourt, France of Medicine New York New York, USA ISSN 0945-6082 ISSN 2196-3096 (electronic) Research and Perspectives in Neurosciences ISBN 978-3-319-28801-7 ISBN 978-3-319-28802-4 (eBook) DOI 10.1007/978-3-319-28802-4 Library of Congress Control Number: 2016936685 Springer Cham Heidelberg New York Dordrecht London © The Editor(s) (if applicable) and The Author(s) 2016. This book is published with open access at SpringerLink.com. Open Access This book is distributed under the terms of the Creative Commons Attribution- Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The images or other third party material in this book are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material. This work is subject to copyright. All commercial rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
    [Show full text]
  • Synergistic Gating of Electro‑Iono‑Photoactive 2D Chalcogenide Neuristors : Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity
    This document is downloaded from DR‑NTU (https://dr.ntu.edu.sg) Nanyang Technological University, Singapore. Synergistic gating of electro‑iono‑photoactive 2D chalcogenide neuristors : coexistence of Hebbian and homeostatic synaptic metaplasticity John, Rohit Abharam; Liu, Fucai; Chien, Nguyen Anh; Kulkarni, Mohit Rameshchandra; Zhu, Chao; Fu, Qundong; Basu, Arindam; Liu, Zheng 2018 John, R. A., Liu, F., Chien, N. A., Kulkarni, M. R., Zhu, C., Fu, Q., . Mathews, N. (2018). Synergistic gating of electro‑iono‑photoactive 2D chalcogenide neuristors : coexistence of Hebbian and homeostatic synaptic metaplasticity. Advanced Materials, 30(25), 1800220‑. doi:10.1002/adma.201800220 https://hdl.handle.net/10356/138300 https://doi.org/10.1002/adma.201800220 © 2018 WILEY‑VCH Verlag GmbH & Co. KGaA, Weinheim. All rights reserved. This paper was published in Advanced Materials and is made available with permission of WILEY‑VCH Verlag GmbH & Co. KGaA, Weinheim. Downloaded on 25 Sep 2021 05:30:26 SGT Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: co-existence of Hebbian and Homeostatic Synaptic Metaplasticity Rohit Abraham John1†, Fucai Liu1†, Nguyen Anh Chien1, Mohit R. Kulkarni1, Chao Zhu1, Qundong Fu1, Arindam Basu2, Zheng Liu1, Nripan Mathews1,3* 1 School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 2 School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 3 Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, Singapore 637553 †These authors contributed equally in this work * Corresponding author Prof. Nripan Mathews (Email: [email protected]) Keywords: 2D chalcogenides, Neuromorphic Computing, Hebbian Synaptic Plasticity, Homeostatic regulation, Associative learning 1 Abstract.
    [Show full text]
  • Tuesday Scientific Session Listing:438–626
    TUESDAY SCIENTIFIC SESSION LISTING:438–626 Washington, DC Nov. 11–15 Complete Session Listing Tuesday AM LECTURE Walter E. Washington Convention Center SYMPOSIUM Walter E. Washington Convention Center 438. Bridge Over Troubled Synapses: C1q Proteins, Glud 440. Exciting New Tools and Technologies Emerging From Receptors, and Beyond — CME the BRAIN Initiative — CME Tue. 8:30 AM - 9:40 AM — Hall D Tue. 8:30 AM - 11:00 AM — Ballroom C Speaker: M. YUZAKI, Keio Univ. Sch. of Med. Chair: J. A. GORDON The C1q complement family has emerged as a new class The BRAIN Initiative seeks to reveal how brain cells and of synaptic organizers. C1q is shown to regulate synapse circuits dynamically interact in time and space to shape elimination. In the cerebellum, Cbln1 binds to its pre- and our perceptions and behavior. BRAIN investigators are postsynaptic receptors neurexin (Nrx) and the δ2 glutamate accelerating the development and application of new tools receptor (GluD2), respectively. The Nrx/Cbln1/GluD2 and neurotechnologies to tackle these challenges. This tripartite complex across the synaptic gap is essential not symposium highlights advances that will enable exploration only for synapse formation, but also for synaptic plasticity. of how the brain records, stores, and processes vast Similar mechanisms are beginning to be revealed for amounts of information, shedding light on the complex links other Cbln- and C1q-like proteins in various circuits in the between brain function and behavior. forebrain. 8:30 440.01 Introduction. 8:35 440.02 High density carbon fiber electrode array for the SYMPOSIUM Walter E. Washington Convention Center detection of electrophysiological and dopaminergic activity.
    [Show full text]
  • Reversal of Cocaine-Associated Synaptic Plasticity in Medial Prefrontal Cortex Parallels Elimination of Memory Retrieval
    Neuropsychopharmacology (2017) 42, 2000–2010 © 2017 American College of Neuropsychopharmacology. All rights reserved 0893-133X/17 www.neuropsychopharmacology.org Reversal of Cocaine-Associated Synaptic Plasticity in Medial Prefrontal Cortex Parallels Elimination of Memory Retrieval 1,2 *,1,3 James M Otis and Devin Mueller 1Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA; 2Department of Psychiatry, University of North Carolina- 3 Chapel Hill, Chapel Hill, NC, USA; Department of Basic Sciences, Neuroscience Division, Ponce Health Sciences University-School of Medicine, Ponce Research Institute, Ponce, Puerto Rico Addiction is characterized by abnormalities in prefrontal cortex that are thought to allow drug-associated cues to drive compulsive drug seeking and taking. Identification and reversal of these pathologic neuroadaptations are therefore critical for treatment of addiction. Previous studies using rodents reveal that drugs of abuse cause dendritic spine plasticity in prelimbic medial prefrontal cortex (PL-mPFC) pyramidal neurons, a phenomenon that correlates with the strength of drug-associated memories in vivo. Thus, we hypothesized that cocaine-evoked plasticity in PL-mPFC may underlie cocaine-associated memory retrieval, and therefore disruption of this plasticity would prevent retrieval. Indeed, using patch clamp electrophysiology we find that cocaine place conditioning increases excitatory presynaptic and postsynaptic transmission in rat PL-mPFC pyramidal neurons. This was accounted for by increases in excitatory presynaptic release, paired- pulse facilitation, and increased AMPA receptor transmission. Noradrenergic signaling is known to maintain glutamatergic plasticity upon reactivation of modified circuits, and we therefore next determined whether inhibition of noradrenergic signaling during memory reactivation would reverse the cocaine-evoked plasticity and/or disrupt the cocaine-associated memory.
    [Show full text]
  • Biologically Inspired Neural Control Network for a Bipedal Walking Model
    BIOLOGICALLY INSPIRED NEURAL CONTROL NETWORK FOR A BIPEDAL WALKING MODEL by WEI LI Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Advisor: Dr. Roger D. Quinn Department of Mechanical and Aerospace Engineering CASE WESTERN RESERVE UNIVERSITY January, 2017 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of Wei Li Candidate for the degree of Doctor of Philosophy* Committee Chair Roger Quinn Committee Member Musa Audu Committee Member Kiju Lee Committee Member Richard Bachmann Date of Defense: 11/28/2016 *We also certify that written approval has been obtained for any proprietary material contained therein. Table of Contents Table of Contents ....................................................................................................... i List of Tables ............................................................................................................. v List of Figures .......................................................................................................... vi Abstract .................................................................................................................... xi Chapter 1 Introduction .......................................................................................... 1 1.1 Research Objectives .................................................................................... 4 1.2 Dissertation Structure .................................................................................
    [Show full text]
  • Active Memory Processing on Processing Memory Active Active Memory Processing
    FLORIAN FIEBIG DOCTORAL THESIS Active Memory Processing on Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity FLORIAN FIEBIG Multiple Time - scales Simulated Cortical in NetworksHebbian with Plasticity TRITA-EECS-AVL-2018:91 ISBN 978-91-7873-030-8 Active Memory Processing on Multiple Time-scales in Simulated Cortical Networks with Hebbian Plasticity Thesis Submitted to KTH Royal Institute of Technology and University of Edinburgh towards PhD in Computer Science and Doctor of Philosophy By FLORIAN FIEBIG FLORIANunder the guidance FIEBIG of Prof. Anders Lansner KTH Stockholm and Prof. Mark van Rossum University of Edinburgh Stockholm, Sweden 2018 Abstract This thesis examines declarative memory function, and its underlying neural activity and mechanisms in simulated cortical networks. The included simulation models utilize and synthesize proposed universal computational principles of the brain, such as the modularity of cortical circuit organization, attractor network theory, and Hebbian synaptic plasticity, along with selected biophysical detail from the involved brain areas to implement functional models of known cortical memory systems. The models hypothesize relations between neural activity, brain area interactions, and cognitive memory functions such as sleep-dependent memory consolidation, or specific working memory tasks. In particular, this work addresses the acutely relevant research question if recently described fast forms of Hebbian synaptic plasticity are a possible mechanism
    [Show full text]