Computational Models of Brain and Behavior

Computational Models of Brain and Behavior

Edited by Dr Ahmed A. Moustafa

This edition first published 2018© 2018 John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication DataNames: Moustafa, Ahmed, 1977- editor.Title: Computational models of brain and behavior / [edited by] Ahmed Moustafa.Description: First edition. | Hoboken, NJ : Wiley, [2017] | Includes bibliographical references and index. | Identifiers: LCCN 2017012507 (print) | LCCN 2017014332 (ebook) | ISBN 9781119159070 (pdf) | ISBN 9781119159186 (epub) | ISBN 9781119159063 (hardback)Subjects: LCSH: Computational neuroscience. | Neurobiology—Mathematical models.Classification: LCC QP357.5 (ebook) | LCC QP357.5 .C627 2017 (print) | DDC 612.8/233—dc23 LC record available at https://lccn.loc.gov/2017012507

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vii

Notes on Contributors xiAcknowledgment xxiiiIntroduction xxvAhmed A. Moustafa

Part I Models of Brain Disorders 1

1 A Computational Model of Dyslexics’ Perceptual Difficulties as Impaired Inference of Sound Statistics 3Sagi Jaffe-Dax, Ofri Raviv, Yonatan Loewenstein, and Merav Ahissar

2 Computational Approximations to Intellectual Disability in Down Syndrome 15Ángel E. Tovar, Ahmed A. Moustafa, and Natalia Arias-Trejo

3 Computational Psychiatry 29Robb B. Rutledge and Rick A. Adams

4 Computational Models of Post-traumatic Stress Disorder (PTSD) 43Milen L. Radell, Catherine E. Myers, Jony Sheynin, and Ahmed A. Moustafa

5 Reward Processing in Depression 57The Computational ApproachChong Chen and Taiki Takahashi

6 Neurocomputational Models of Schizophrenia 73Ahmed A. Moustafa, Błażej Misiak, and Dorota Frydecka

7 Oscillatory Dynamics of Brain Microcircuits 85Modeling Perspectives and Neurological Disease ConsiderationsFrances K. Skinner and Alexandra Pierri Chatzikalymniou

8 Computational Models of Pharmacological and Immunological Treatment in Alzheimer’s Disease 99Vassilis Cutsuridis and Ahmed A. Moustafa

9 Modeling Deep Brain Stimulation for Parkinson’s Disease 109Volume Conductor, Network, and Mean-Field ModelsMadeleine M. Lowery

Contents

Contentsviii

10 The Development of Medications for Parkinson’s Disease Using Computational Modeling 125Mubashir Hassan and Ahmed A. Moustafa

11 Multiscale Computer Modeling of Epilepsy 139M. Sanjay, Samuel A. Neymotin, Srinivasa B. Krothapalli, and William W. Lytton

Part II Neural Models of Behavioral Processes 151

12 Simple Models of Sensory Information Processing 153Danke Zhang, Malte J. Rasch, and Si Wu

13 Motion Detection 171An Artificial Recurrent Neural Network ApproachJeroen Joukes and Bart Krekelberg

14 Computation in the Olfactory System 185Christiane Linster

15 Computational Models of Olfaction in Fruit Flies 199Ankur Gupta, Faramarz Faghihi, and Ahmed A. Moustafa

16 Multisensory Integration 215How the Brain Combines Information Across the SensesRyan L. Miller and Benjamin A. Rowland

17 Computational Models in Social Neuroscience 229Jin Hyun Cheong, Eshin Jolly, Sunhae Sul, and Luke J. Chang

18 Sleep is For the Brain 245Contemporary Computational Approaches in the Study of Sleep and Memory and a Novel “Temporal Scaffolding” HypothesisItamar Lerner

19 Models of Neural Homeostasis 257Hazem Toutounji

Part III Models of Brain Regions and Neurotransmitters 271

20 Striatum 273Structure, Dynamics, and FunctionJyotika Bahuguna and Arvind Kumar

21 Amygdala Models 285Vinay Guntu, Feng Feng, Adel Alturki, Ajay Nair, Pranit Samarth, and Satish S. Nair

22 Cerebellum and its Disorders 303A Review of Perspectives from Computational NeuroscienceShyam Diwakar and Ahmed A. Moustafa

Contents ix

23 Models of Dynamical Synapses and Cortical Development 321Radwa Khalil, Marie Z. Moftah, Marc Landry, and Ahmed A. Moustafa

24 Computational Models of Memory Formation in Healthy and Diseased Microcircuits of the Hippocampus 333Vassilis Cutsuridis

25 Episodic Memory and the Hippocampus 345Naoyuki Sato

26 How Do We Navigate Our Way to Places? 357Developing a New Model to Study Place Field Formation in Hippocampus Including the Role of AstrocytesFariba Bahrami and Shiva Farashahi

27 Models of Neuromodulation 373Michael C. Avery and Jeffrey L. Krichmar

28 Neural Circuit Models of the Serotonergic System 389From Microcircuits to CognitionPragathi Priyadharsini Balasubramani, V. Srinivasa Chakravarthy, KongFatt Wong-Lin, Da-Hui Wang, Jeremiah Y. Cohen, Kae Nakamura, and Ahmed A. Moustafa

Part IV Neural Modeling Approaches 401

29 A Behavioral Framework for Information Representation in the Brain 403Frédéric Alexandre,

30 Probing Human Brain Function with Artificial Neural Networks 413Umut Güçlü and Marcel van Gerven

31 Large-scale Computational Models of Ongoing Brain Activity 425Tristan T. Nakagawa, Mohit H. Adhikari, and Gustavo Deco

32 Optimizing Electrical Stimulation for Closed-loop Control of Neural Ensembles 439A Review of Algorithms and ApplicationsSeif Eldawlatly

33 Complex Probabilistic Inference 453From Cognition to Neural ComputationSamuel J. Gershman and Jeffrey M. Beck

34 A Flexible and Efficient Hierarchical Bayesian Approach to the Exploration of Individual Differences in Cognitive-model-based Neuroscience 467Alexander Ly, Udo Boehm, Andrew Heathcote, Brandon M. Turner, Birte Forstmann, Maarten Marsman, and Dora Matzke

35 Information Theory, Memory, Prediction, and Timing in Associative Learning 481Jason T. Wilkes and C. R. Gallistel

Contentsx

36 The Utility of Phase Models in Studying Neural Synchronization 493Youngmin Park, Stewart Heitmann, and G. Bard Ermentrout

37 Phase Oscillator Network Models of Brain Dynamics 505Carlo R. Laing

38 The Neuronal Signal and Its Models 519Igor Palmieri, Luiz H. A. Monteiro, and Maria D. Miranda

39 History Dependent Neuronal Activity Modeled with Fractional Order Dynamics 531Seth H. Weinberg and Fidel Santamaria

Index 549

xi

Notes on Contributors

Rick A. Adams is an academic clinical lec-turer in psychiatry at University College London (UCL). He studied medicine at Cambridge University and did his clinical training and PhD at University College London, the latter under Professor Karl Friston. His research focuses on using tech-niques from computational psychiatry to understand schizophrenia and psychosis, and he co-organizes a computational psychi-atry course at UCL.

Mohit H. Adhikari is a postdoctoral researcher at the Center for Brain and Cognition at the University of Pompeu Fabra. His current research focus is computational modeling of resting state functional data, particularly from human stroke patients.

Merav Ahissar is a professor of psychology, she holds the Joseph H. and Belle R. Braun Chair in Psychology, and is a member of the Edmond and Lily Safra Center for Brain Sciences at the Hebrew University. She stud-ies theories of perceptual learning, and developed in collaboration with Professor Shaul Hochstein, the Reverse Hierarchy Theory of perception and perceptual learn-ing, initially for vision and later for audition. She also studies abnormal learning processes among populations with learning disabilities, with an emphasis on reading disability. She developed the Anchoring Hypothesis Theory, which proposes that dyslexics use sound statistics inefficiently in forming audi-tory simple and linguistic precepts. She uses behavioral, computational, event-related potential (ERP), and imaging tools.

F. Frédéric Alexandre is a director of research at Inria, the French Institute for Research in Computer Science and Auto-mation . He is the head of the Mnemos yne group, working in computational neurosci-ence in the Bordeaux Neurocampus, at the Institute of Neurodegenerative Diseases. His research interests are concerned with the emergence of intelligent behavior, by means of computational neuroscience, machine learning, artificial intelligence, and cognitive modeling, in tight loop with neuroscience and the medical domain.

Adel Alturki is a PhD student in electrical engineering at the University of Missouri-Columbia. He obtained dual Master’s degrees in electrical engineering and applied mathe-matics from Western Michigan University in 2011. He is presently on leave from his posi-tion as instructor at Yanbu Industrial College, Saudi Arabia. His research interests include computational neuroscience, artificial intel-ligence, and control systems.

Natalia Arias-Trejo is a professor in the Faculty of Psychology, National Autonomous University of Mexico (UNAM). Her fields of research include psycholinguistics, early lex-ical networks, and intellectual disability. Key publications are Abreu-Mendoza, R. A. & Arias-Trejo, N. (2015). Numerical and Area Comparison Abilities in Down Syndrome. Research in Developmental Disabilities; Arias-Trejo, N., Cantrell, L. M., Smith, L., & Alva-Canto, E. A. (2014). Early Comprehen-sion of the Spanish Plural. Journal of Child Language; Arias-Trejo, N. & Plunkett, K.

Notes on Contributorsxii

(2013). What’s in a Link: Associative and Semantic Priming Effects in the Infant Lexicon. Cognition.

Michael C. Avery received a BSc in mathe-matics and biochemistry in 2007 from Virginia Tech, a PhD in cognitive neurosci-ence from the University of California, Irvine in 2013, and is currently a postdoctoral researcher in the Systems Neurobiology Laboratories at the Salk Institute. He is inter-ested in understanding the circuit-level com-putations that give rise to cognitive functions and how their failures may lead to mental disorders.

Fariba Bahrami received her PhD in bio-medical engineering from the University of Tehran. She was awarded a scholarship for her PhD at the Technical University of Munich, Germany. Since 2013 she has been an associate professor at the University of Tehran. Her main fields of interest are bio-logical system modeling, computational neu-roscience, human motor control, and rehabilitation. In 2012, she was awarded the Institute of Electrical and Electronics Engineers (IEEE) Women-In-Engineering Award for her tremendous contribution to biomedical engineering in Iran.

Jyotika Bahuguna is a researcher at Forschungszentrum Jülich, Germany. She received her doctoral degree in computa-tional neuroscience from Bernstein Center Freiburg and KTH Royal Institute of Technology, Stockholm, Sweden, in 2016. She in interested in the structure–function relationship in neuronal networks, the role of spike-time dependent plasticity on network function, and neural coding. She is currently developing large-scale mathematical models to investigate basal ganglia activity dynamics in healthy and pathological states, especially Parkinson’s disease.

Jeffrey M. Beck received a BSc in mathemat-ics from Harvey Mudd College and a PhD in applied mathematics from Northwestern University. He was a postdoctoral fellow in

the Department of Brain and Cognitive Sciences at the University of Rochester and  also at the Gatsby Computational Neuroscience Unit at UCL. He is now an assistant professor of neurobiology and bio-medical engineering at Duke University.

Udo Boehm is a PhD candidate in mathe-matical psychology. He received his bache-lor’s degree in psychology in 2009 and his Master’s degree in behavioral and cognitive neurosciences (cognitive modeling) in 2012. His main research interests are mathematical models of decision making and Bayesian statistics.

Luke J. Chang is currently an assistant pro-fessor in psychological and brain sciences at Dartmouth College. He completed a BA at Reed College, an MA at the New School for Social Research, a PhD in clinical psychology at the University of Arizona, a predoctoral clinical internship in behavioral medicine at the University of California—Los Angeles (UCLA), and a postdoc at the University of Colorado Boulder. His research program is focused on understanding the neurobiologi-cal and computational mechanisms underly-ing emotion and social interactions.

Chong Chen, MD, PhD (medicine, Hokkaido University), was formerly at the Department of Psychiatry, Hokkaido University Graduate School of Medicine, and is now a research scientist at Riken Brain Science Institute. He studies the neurobiological basis of stress and depression and is particularly interested in computational psychiatry.

Jin Hyun Cheong graduated from Princeton University with a BA in psychology and cer-tificates in neuroscience and finance. Postgraduation, he worked as a research assistant at the Princeton Neuroscience Institute and investigated the computational and neural foundations of optimal human decision making. Currently, he is a graduate student at Dartmouth College and is inter-ested in applying computational, behavioral, psychophysiological, and neuroimaging

Notes on Contributors xiii

methods to investigate how emotions and social cognition impact economic choices and behavior.

Jeremiah Y. Cohen is an assistant professor in the Solomon H. Snyder Department of Neuroscience and the Brain Science Institute at the Johns Hopkins University School of Medicine. His laboratory studies neurophys-iology underlying reward and decision mak-ing. He was trained as a postdoctoral fellow at Harvard University and received his PhD in neuroscience at Vanderbilt University.

Vassilis Cutsuridis is an accomplished com-putational neuroscientist and cognitive sys-tems researcher at the Foundation for Research and Technology Hellas (FORTH). His research aims to decipher how brain cir-cuits and patterns of neural activity give rise to mental experience and how such an under-standing can help design brain-mimetic algorithms for complex data analysis and sys-tems with autonomous behavior. He has published over 70 peer reviewed papers and four edited books.

Gustavo Deco is Institució Catalana de Recerca i Estudis Avançats (ICREA) research professor and full professor at the Universitat Pompeu Fabra, where he heads the Computational Neuroscience Group and directs the Center for Brain and Cognition. Recognized as a world leader in computa-tional neuroscience, he has pioneered work in dynamical modeling of human brain activ-ity. He is a European Reaerch Council Advanced Grantee and core member of the Human Brain Project. He has published four books, over 210 international journal papers, and 30 book chapters.

Shyam Diwakar is an assistant professor and  lab director of the Computational Neuroscience Laboratory, School of Biotechnology and a faculty fellow at the Center for International Programs at Amrita University, India. He is a co-investigator of a National virtual labs initiative and other pro-jects funded by the Department of Science

and Technology (DST) and the Department of Biotechnology (DBT), Government of India. He was awarded the Sir Visvesvaraya Young Faculty Research Fellowship in April 2016 by DeitY, Government of India, and the Nvidia Innovation award in 2015.

Seif Eldawlatly is an assistant professor at the Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt. He received his PhD in electrical and computer engineer-ing from Michigan State University in 2011 and his MSc and BSc degrees in computer and systems engineering from Ain Shams University in 2006 and 2003, respectively. His research focuses on developing machine learning and signal processing algorithms for  brain–machine interfaces and visual prostheses.

G. Bard Ermentrout received a BA and MA in mathematics from the Johns Hopkins University, and a PhD in biophysics and the-oretical biology from the University of Chicago in 1979. He is a distinguished uni-versity professor of computational biology and a professor of mathematics at the University of Pittsburgh. He is interested in the applications of dynamical systems to problems in biology with a particular empha-sis in neuroscience. He is an avid, if feckless, gardener.

Faramarz Faghihi obtained his BSc and MSc in medical biochemistry. After that, he studied computational biology as a research assistant at Heidelberg University, Germany. He did his PhD in theoretical and computa-tional neuroscience at the Physics Institute, Gottingen University, Germany. His PhD project was on the modeling of information processing in the Drosophila olfactory sys-tem. Currently, he is an assistant professor at the Shahid Beheshti University, Tehran, Iran.

Shiva Farashahi received a BE in electrical engineering from the Ferdowsi University of Mashhad, Mashad, Iran, in 2011, and an MSc in biomedical engineering from the

Notes on Contributorsxiv

University of Tehran, Tehran, Iran, in 2013. She is currently a PhD student at the Department of Psychological and Brain Sciences, Dartmouth College, Hanover, USA. Her research interests include computational modeling of neural circuits underlying cog-nitive processes, and decision making.

Feng Feng is a PhD student at the University of Missouri-Columbia, after practicing for 6  years as an analog/radio frequency engi-neer for Huawei Technologies Co., Ltd. His research interests include computational neuroscience, modeling and control of non-linear dynamic systems, and systems analy-sis. To date, he has authored three journal articles.

Birte Forstmann is professor of cognitive neurosciences at the University of Amsterdam (UvA). She earned her PhD in 2006 at the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. After completing her postdoc in 2008 at UvA, she became a tenured research fellow at the Cognitive Science Center Amsterdam with a focus on model-based cognitive neurosciences. Since then she has contributed to a range of topics in cognitive neuroscience, experimental psychology, and mathematical psychology.

Dorota Frydecka, MA, MSc Eng, MD, PhD, is a medical doctor, psychologist, and com-puter scientist, currently working in the Department of Psychiatry at Wroclaw Medical University. Her main interest is related to the genetic basis of psychotic dis-orders in relation to immune system and cognitive functions (Ministry of Science and Higher Education, Foundation of Polish Science, National Science Center). She has received numerous awards for her research achievements (i.e., European Psychiatric Association, European College of Neuropsychopharmacology).

C. R. Gallistel is distinguished professor emeritus at Rutgers University. He obtained his PhD in behavioral neuroscience from Yale University in 1963. He joined the

Psychology Faculty at the University of Pennsylvania, where he rose through the ranks to professor and chair. Subsequently, he was distinguished professor at UCLA and then at Rutgers, where he co-chaired the Center for Cognitive Science. His current research develops highly automated systems for screening genetically altered mice for alterations in basic cognitive functions.

Samuel J. Gershman received a BA from Columbia University and a PhD in psychol-ogy and neuroscience from Princeton University. He was a postdoctoral fellow in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT) and is currently an assis-tant professor in the Department of Psychology and Center for Brain Science at Harvard University.

Umut Güçlü is a PhD candidate at Radboud University, the Donders Institute for Brain, Cognition and Behaviour. Umut has a Master’s degree in cognitive neuroscience and a bachelor’s degree in artificial intelli-gence. Umut’s research combines cognitive neuroscience and artificial intelligence tech-niques such as functional magnetic reso-nance imaging and artificial neural networks for studying how the human brain processes sensory information to represent and under-stand the world around us.

Vinay Guntu is a PhD student at the University of Missouri-Columbia. He has twice co-taught the computational neuro-science course, focusing on the software and  hardware labs. His research interests include computational neuroscience, sys-tems modeling, feedback controls, and emer-gent networks.

Ankur Gupta is an Edmond and Lily Safra Center for Brain Sciences postdoctoral fel-low at the Hebrew University of Jerusalem, Israel. His long-term goal is directed toward understanding the role of decision making in movement production and control. Currently, he is working on the motor con-trol of facial expression in primates. In the

Notes on Contributors xv

past, he studied how multi-finger forces are generated and coordinated using neural net-work methods.

Mubashir Hassan received his Master’s degree in pharmaceutical chemistry from Government College University Lahore in 2012. During his time in molecular science and bioinformatics (IMSB), he developed interests in the field of computational mode-ling, dynamic simulation, and computer aided drug designing techniques. Now, he is working on models of the pathways of neuro-degenerative disorders such as Alzheimer’s disease in order to come up with novel drugs through computational modeling and simu-lation studies.

Andrew Heathcote was appointed as a research chair at University of Tasmania in 2015, where he founded the Tasmanian Cognition Laboratory (TasCL.org). He is also a professor at the University of Newcastle, where he founded the Newcastle Cognition Laboratory (NewCL.org) in 1997. His cur-rent research focuses on human memory and skill acquisition, and on the neural and cog-nitive processes that enable people to make rapid choices.

Stewart Heitmann received a BSc in com-puter science (1994) and a BSc in psychology (2007) from the University of Sydney. He was awarded a PhD from the University of New South Wales in 2013. He undertook postdoc-toral training at the University of Pittsburgh. He is currently a research associate at the Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute. His research interests include pat-tern formation in neural systems and its application to systems neuroscience.

Sagi Jaffe-Dax studies in the ELSC neural computation PhD program (formerly known as Interdisciplinary Center for Neural Computa tion [ICNC]) at the Hebrew University of Jerusalem. He is expected to fin-ish his dissertation soon and to embark on new research in the field of developmental neuroscience.

Eshin Jolly graduated from the University of Rochester with a BA in psychology and cog-nitive neuroscience. Subsequently, he worked as a research assistant at Harvard University investigating the neural systems underlying social reasoning. Currently he is a graduate student at Dartmouth College, where he employs a diverse set of approaches includ-ing large-scale online experiments, computa-tional modeling, and machine learning to understand how individuals make social decisions and derive value from social experiences.

Jeroen Joukes obtained his PhD in neurosci-ence at the Center for Molecular and Behavioral Neuroscience, Rutgers University—Newark, USA. His research interests include neural computations in space and time enabled by recurrent connectivity. He applies machine learning algorithms to neural data to gain understanding of the computations of the neu-ral system.

Radwa Khalil is a PhD student and teaching assistant at Rutgers University—Newark, USA. She is interested in pursuing her research at a postgraduate level in transla-tional and cognitive neuroscience using mul-tidisciplinary approaches. She is seeking to combine state-of-the-art techniques for investigating physiological, cognitive, and/or psychological questions from clinical, educa-tional, and socio-economic perspectives.

Bart Krekelberg is a professor of neurosci-ence at the Center for Molecular and Behavioral Neuroscience, Rutgers University— Newark, USA. His interests include the com-putations enabled by the recurrent connectiv-ity of neural networks, the mechanisms underlying visual stability, and transcranial current stimulation. His laboratory combines a range of methods, including extracellular recordings in awake, behaving primates, func-tional imaging, quantitative psychophysics, transcranial stimulation, and computational modeling.

Jeffrey L. Krichmar received a BSc in com-puter science in 1983 from the University of

Notes on Contributorsxvi

Massachusetts at Amherst, an MSc in com-puter science from the George Washington University in 1991, and a PhD in computa-tional sciences and informatics from George Mason University in 1997. Currently, he is a professor in the Departments of Cognitive Sciences and Computer Science at the University of California, Irvine. His research interests include neurorobotics, models of cognition, and neuromorphic engineering.

Srinivasa B. Krothapalli is working in the Department of Neurological Sciences, Christian Medical College, Vellore, India as a senior scientist. He is the head of the Neurophysiology Laboratory and does both clinical and basic research.

Arvind Kumar is an assistant professor of computational neuroscience at KTH Royal Institute of Technology, Stockholm, Sweden. He is investigating the role of connectivity and dynamics in the transfer of information between neuronal networks, and how neu-ronal network dynamics can be controlled by external stimulation. One of the goals of his research is to develop mathematical models to understand the mechanisms underlying the emergence of disease-related aberrant network activity in the brain.

Carlo R. Laing received his PhD in applied mathematics from the University of Cambridge in 1998, with a thesis on coupled oscillator networks. After postdoctoral posi-tions in the UK and at the Universities of Pittsburgh and Ottawa, he joined Massey University in 2002, where he is now an asso-ciate professor. His research interests cover nonlinear dynamics and computational neu-roscience. He received the J. H. Michell Medal for outstanding new researchers from ANZIAM in 2008.

Marc Landry is the deputy-director of the IINS Institute and of the Bordeaux Imaging Center, University of Bordeaux, France (http://www.iins.u-bordeaux.fr/Team-leader-Marc-Landry-90). He is also the coordinator of an international Master’s program in

Neuroscience at the University of Bordeaux. One of his research focuses is to investigate the inhibition–excitation balance that regu-lates the activity of spinal neurons. Currently, he is the president of the Mediterranean Neuroscience Society (MNS) (www.mnsociety.net).

Itamar Lerner, PhD, is a postdoctoral researcher at the Center for Molecular and Behavioral Neuroscience, Rutgers University—Newark. His research includes combining neural network modeling with human behav-ioral experimentation in uncovering the mechanisms that contribute to learning and memory. In particular, he investigates the special role of sleep in enhancing cognitive functions of various levels of complexity, from simple paired-associates learning to gist extraction, language, and insight.

Christiane Linster is a professor of neurobi-ology and behavior at Cornell University. She was born and raised in Luxembourg, Luxembourg. She obtained a Master’s in electrical engineering from the Technical University in Graz, Austria and a PhD in applied physics from the Pierre and Marie Curie University in Paris, France. After her appointments as a research associate at Harvard University and Boston University she moved to the Department of Neurobiology and Behavior at Cornell University. Her labo-ratory studies the neuromodulation of sen-sory perception of smell using computational and experimental approaches.

Yonatan Loewenstein received his PhD from the Hebrew University in 2004. Between 2004 and 2006 he was a postdoctoral fellow at MIT. Since 2007, he has held a faculty position in  the Departments of Neurobiology and Cognitive Sciences, and is a member of the ELSC for Brain Sciences and the Federmann Center for the Study of Rationality at the Hebrew University of Jerusalem, Israel.

Madeleine M. Lowery is a professor in the School of Electrical and Electronic Engineering, University College Dublin. Her

Notes on Contributors xvii

research involves exploring nerve and mus-cle activity through mathematical modeling, analysis, and experimentation, to increase understanding of neuromuscular activity in healthy and diseased states, and to develop novel and improved rehabilitation strategies. Her research interests include electromyogra-phy, myoelectric prosthetic control, bioelectro-magnetics, electrical stimulation, deep brain stimulation, and neural control of movement.

Alexander Ly is a PhD candidate, supervised by Eric-Jan Wagenmakers, at the University of Amsterdam. He received his interdiscipli-nary natural and social sciences bachelor’s degree with a major in mathematics in 2009 and his Master’s degree in mathematics in  2011. His main research interests are Bayesian model selection, machine learn-ing, mathematical modeling, and asymptotic statistics.

William W. Lytton is a professor in physiol-ogy, pharmacology and neurology at State University of New York System (SUNY) Downstate, and works as a clinical neurolo-gist at Kings County Hospital, seeing patients with a variety of brain ailments. His research is in computational neuroscience with a focus on the application of multiscale modeling to various disorders of the brain, including Alzheimer’s, stroke, Parkinson’s, epilepsy, and schizophrenia. He is author of From Computer to Brain, a basic textbook in the field.

Dora Matzke obtained her PhD in mathe-matical psychology in 2014 at the University of Amsterdam. She is currently assistant professor in the Psychological Methods Department at the University of Amsterdam. Her research focuses on formal models of response inhibition, multinomial processing tree models, and Bayesian inference.

Maarten Marsman is an assistant professor in psychometrics. He received his bachelor’s degree in psychology in 2007, his Master’s degree in survey methods in 2009, and suc-cessfully defended his doctoral thesis in psychometrics in 2014. His main research

interests are Bayesian and computational sta-tistical methods in general, psychometrics, and educational measurement.

Ryan L. Miller graduated from North Dakota State University with a bachelor’s degree in psychology and earned a doctorate in neurobiology and anatomy from Wake Forest School of Medicine, where he is cur-rently employed as a postdoc. His primary research interests involve understanding the mechanistic bases for multisensory integra-tion and how they shape the neural, and ulti-mately behavioral, response.

Maria D. Miranda was born in Florianópolis, Brazil. She received BSc (1983) and MSc degrees (1987) from Universidade Federal de Santa Catarina, and degree PhD (1996) from Escola Politécnica da Universidade de São Paulo (USP), all in electrical engineering. Currently, she is an assistant professor in the Department of Telecommunications and Control Engineering, Escola Politécnica, USP. Her research interests include statistical sig-nal processing and adaptive filtering theory and applications.

Błażej Misiak, MD, PhD, is a researcher in the Department of Genetics (Wroclaw Medical University, Poland). His main inter-ests include cognitive neuroscience, genetics and epigenetics of schizophrenia. He is the author of several articles published in inter-national journals. Błażej has received a num-ber of prestigious awards including the Polish Ministry of Health scholarship, the START scholarship provided by the Foundation for  Polish Science, and the European College of Neuropsychopharmacology (ECNP) Fellowship Award.

Marie Z. Moftah is an associate professor in the Zoology Department, Faculty of Science, Alexandria University, and an invited professor at Bordeaux University, France (http://loop.frontiersin.org/people/10502/overview). She is a former president of the Mediterranean Neuroscience Society (MNS) (www.mnsociety.net). She obtained her PhD from New York

Notes on Contributorsxviii

Medical College, Valhalla, USA in develop-mental biology (2001). Then, she pursued her postdoc at Bordeaux University, France in neuroscience: spinal cord regeneration after complete transection (2004–2005).

Luiz H. A. Monteiro was born in São Paulo, Brazil, in 1967. He received his PhD in physics from the Instituto de Física da Universidade de São Paulo in 1995. He is cur-rently professor at the Escola de Engenharia da Universidade Presbiteriana Mackenzie and at the Escola Politécnica da Universidade de São Paulo. His research topics include studies on the dynamical behaviors appear-ing in complex networks of biological and electronic oscillators.

Ahmed A. Moustafa is currently a senior lecturer in cognitive and behavioral neurosci-ence at MARCS Institute for Brain, Behaviour, and Development and the School of Social Sciences and Psychology, Western Sydney University. Ahmed has published over 110 papers, some in high-ranking journals includ-ing Science, Proceedings of the National Acad emy of Science, the Journal of Neuro­science, Brain, Nature (Parkinsons’ disease), Neu roscience and Biobehavioral Reviews, among others. Ahmed’s research focus is on com putational and experimental neurosci-ence, focusing on brain disorders.

Catherine E. Myers is a research scientist with the Department of Veterans Affairs New Jersey Health Care System, East Orange, USA, and professor of pharmacology, physi-ology and neuroscience at New Jersey Medical School of Rutgers University. She received her PhD in neural systems engineer-ing from Imperial College of the University of London, UK. Her research focuses on the brain substrates of human learning and memory with application to understanding neuropsychiatric disorders.

Ajay Nair completed his BSc in biology, with a focus in engineering, at the Carnegie Mellon University. He is presently a 4th year MD student at Ross University and has been

conducting research in neuroscience in parallel, including providing assistance on computational neuroscience projects. He is the author of two journal publications. His research interests include computational neuroscience and neuropsychiatry.

Satish S. Nair a professor of electrical and computer engineering, and director of the Center for Computational Neurobiology at the University of Missouri—Columbia. He works in the area of computational neurosci-ence, and spans molecular, cellular, network, and behavioral levels. He is author of 151 ref-ereed articles (79 journal, 72 conference), and 83 posters and abstracts. He is also active in research and educational training in neu-roscience from faculty to K-12 levels.

Tristan T. Nakagawa is specially appointed researcher at Osaka University and invited researcher at the Center for Information and Neural Networks. He has studied oscillations and spontaneous dynamics in whole-brain computational models and currently studies pain perception and biomarkers in the con-text of brain–immune interactions.

Kae Nakamura is professor of neurophysiol-ogy at Kansai Medical University, Osaka, Japan. Dr Nakamura received an MD from Tokyo Medical University, and a PhD from Juntendo University, Tokyo. Her studies have focused on the neuronal mechanisms of higher cognitive function in primate, includ-ing procedural learning, the interaction between vision and saccadic eye movement, the executive function under conflict, and the role of dopamine and serotonin for reward- and punishment-based modulation in action.

Samuel A. Neymotin is research assistant professor in the Physiology and Pharmacology Department at SUNY Downstate Medical Center. He received a BSc in computer sci-ence from Queens College in 2001, an MSc in computer science from Columbia University in 2005, and a PhD in biomedical engineering from SUNY Downstate/NYU-Poly in 2012. He subsequently joined Yale

Notes on Contributors xix

University as a postdoctoral associate in neu-robiology. He then joined SUNY Downstate Medical Center as a research assistant pro-fessor, where he has worked on computa-tional neuroscience research.

Igor Palmieri was born in São Paulo, Brazil, in 1984. He received his MSc in systems engi-neering from the Escola Politécnica da Universidade de São Paulo in 2015, and his BEng in computer engineering from the same university in 2009. Currently, he is assistant researcher at the Escola Politécnica da Universidade de São Paulo. His research top-ics include neuronal signal modeling, artifi-cial neural networks, and adaptive filtering.

Youngmin Park received a BSc and an MSc in applied mathematics in 2013 from Case Western Reserve University. He is currently a graduate student at the University of Pittsburgh, where his research interests include weakly coupled oscillators and neu-ral field models. Outside of research he enjoys pottery and playing the ukulele.

Alexandra Pierri Chatzikalymniou is a PhD candidate in Dr Frances Skinner’s lab at the Krembil Research Institute, University Health Network, and in the Department of Physiology, University of Toronto, Canada. She is interested in computational modeling of local field potentials in the hippocampus and her objective is to understand generation mechanisms of extracellular fields in healthy states and during disease. Emphasis is given to the relationship between experimentally recorded extracellular fields and computa-tional models.

Pragathi Priyadharsini Balasubramani is currently a postdoctoral scholar working on the principles of reward systems with Professor Benjamin Hayden in the depart-ment of Brain and Cognitive Sciences, University of Rochester, New York. Her PhD  was from the Indian Institute of Technology—Madras (IIT-M) on computa-tional modeling of the roles of dopamine and serotonin in reward, punishment, and risk-

based decision making. Her thesis was guided by Professors Srinivasa Chakravarthy and Balaraman Ravindran at IIT-M.

Milen L. Radell is currently a visiting profes-sor with the Department of Psychology at Niagara University, and received his PhD in behavioral neuroscience from the University at Buffalo, the State University of New York. His research focuses on learning, memory, and decision making, with a focus on anxiety disorders and post-traumatic stress disorder (PTSD), using a combined experimental and computational modeling approach.

Malte J. Rasch is an associate researcher with the State Key Lab of Cognitive Neuroscience and Learning of the Beijing Normal University in China, graduated in biophysics from the Humboldt University in Berlin, and received his PhD in telematics from the Graz University of Technology. His current research focuses on understanding neural information pro-cessing by combining experimental data anal-ysis and computational modeling.

Ofri Raviv is a graduate of the IDF’s Talpiot excellence program. He received his PhD in computational neuroscience from the Hebrew University in 2015. He is an open data activist, and has founded the Open-Knesset website, and the Public Knowledge Workshop foundation.

Benjamin A. Rowland is a graduate of the University of North Carolina at Chapel Hill and the University of Louisiana at Lafayette. He is currently associate professor of neuro-biology and anatomy at the Wake Forest School of Medicine. His research focuses on how the brain learns to integrate information across the senses in real time. He is a resident of Winston-Salem, North Carolina, and is happily married with two kids, and one cat.

Robb B. Rutledge is a senior research asso-ciate at University College London at the Max Planck Centre for Computational Psychiatry and Ageing Research and the Wellcome Trust Centre for Neuroimaging.

Notes on Contributorsxx

He received his PhD in neural science from New York University. His research focuses on developing computational models to understand decision making, learning, and subjective feelings, and examining the under-lying neural mechanisms using neuroimag-ing and pharmacology.

Pranit Samarth is a PhD student in the Electrical Engineering Department at the University of Missouri. He is presently work-ing as a quality engineer at The MathWorks. His research interests include computa-tional neuroscience, robotics, and machine learning algorithms. To date, he has authored four technical articles and three K-12 publications.

M. Sanjay pursued a PhD in bioengineering in the broad area of computational neurosci-ence at Christian Medical College (CMC), Vellore, India. Prior to this, he pursued an MSc in bioengineering at CMC Vellore (2007–2009) and a BTech in electronics and biomedical engineering at Model Engineering College, Ernakulam, India (2000–2004). His specific interests are biophysical modeling of hippocampal activity, neurobiology, and clin-ical studies of neuromuscular systems.

Fidel Santamaria is an associate professor in the Department of Biology at the University of Texas at San Antonio. His background is in computational neuroscience of cerebellar function. Dr Santamaria’s laboratory com-bines experimental and computational approaches with the aim to develop a frame-work to understand history dependence and power-law dynamics at multiple scales of neurobiological interest. Recent work includes studies of anomalous diffusion of glutamate receptors to dynamics of intracel-lular chloride in Purkinje cells.

Naoyuki Sato is a professor in the Department of Complex and Intelligent Systems at Future University Hakodate in Japan. His research interests include computational neurosci-ence, functional modeling of neural synchro-nization, and the experimental data-driven

modeling of spatial cognition and episodic memory.

Jony Sheynin is currently a postdoctoral research fellow in the Department of Psychiatry, University of Michigan and the Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, USA. He received his PhD in biomedical engineering from Rutgers, the State University of New Jersey and the New Jersey Institute of Technology (2014). His research focuses on the cognitive and bio-logical basis of maladaptive behavior patterns in anxiety disorders and PTSD, with a special focus on excessive avoidance responding.

Frances K. Skinner is a senior scientist at the Krembil Research Institute, University Health Network, and a professor at the University of Toronto. She is interested in determining mechanisms underlying the dynamic output of neurons and neuronal networks. The over-all approach of her laboratory involves the use, development, and analysis of mathemati-cal models that have clear links with experi-ment and theory. The present focus is on oscillatory network activities and on inhibi-tory cells in the hippocampus.

V. Srinivasa Chakravarthy obtained his PhD from the University of Texas at Austin and received postdoctoral training at Baylor College of Medicine, Houston. He is cur-rently a professor in the Department of Biotechnology, at the Indian Institute of Technology, Madras. His research interests are in the areas of computational neurosci-ence, computational cardiology, and machine learning. In computational neuroscience, his laboratory focuses on developing multiscale models of basal ganglia to understand Parkinson’s disease.

Sunhae Sul is an assistant professor in psy-chology at Pusan National University. She graduated from Seoul National University in South Korea with an MA in biological psy-chology and a PhD in social psychology. She  completed postdoctoral training in neuroeconomics at the University of Zurich

Notes on Contributors xxi

and Dartmouth College, and was previously a research professor of psychology at Korea University. Sunhae’s research uses a neuro-economic approach to understand the influence of the self and others on social interactions.

Taiki Takahashi, PhD (biophysics, The University of Tokyo), is an associate profes-sor in the Department of Behavioral Science, Research and Education Center for Brain Science, Hokkaido University. He is particu-larly interested in the physics of judgment and decision making, neuroeconomics, and computational psychiatry.

Hazem Toutounji received his BSc in elec-tronic engineering from the University of Aleppo in 2007 and his MSc in computa-tional science from Frankfurt University in 2010. He was granted the title of Dr. rer. nat.  in 2014 with summa cum laude from the Neuroinformatics and Neurocybernetics Departments, Institute of Cognitive Science, University of Osnabrück. He is currently a  postdoctoral researcher in theoretical neuroscience at the Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University.

Ángel E. Tovar received a BSc in psychology and a PhD in experimental psychology from the National Autonomous University of México. His research focuses on developing computational models of typical and atypical learning, memory, and categorization pro-cesses. He is currently an adjunct professor at the National Autonomous University of México.

Brandon M. Turner is an assistant professor in  the Psychology Department at the Ohio State University. He received a BSc from Missouri State University in mathematics and psychology in 2008, and a PhD from the Ohio State University in 2011. His research interests include dynamic models of cogni-tion and perceptual decision making, effi-cient methods for performing likelihood-free and likelihood-informed Bayesian inference,

and unifying behavioral and neural explana-tions of cognition.

Marcel van Gerven was trained as a cogni-tive scientist. His PhD research focused on probabilistic inference in clinical oncology and part of his thesis work was conducted at  UNED, Madrid. As a postdoctoral researcher at the Institute for Computing and Information Sciences he created novel brain–computer interfacing paradigms. He is currently an assistant professor at the Donders Institute for Brain, Cognition and Behaviour, and principal investigator of the  Computational Cognitive Neuroscience laboratory.

Da-Hui Wang is a professor at the School of  Systems Science, and National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China. He received his PhD in systems theory from Beijing Normal University in 2002. He focuses on complexity in neural system, and works on the dynamics underlying neuro-modulation and cognitive functions. He has  spent 1 year in Yale for computational neuroscience.

Seth H. Weinberg is an assistant professor in the Department of Biomedical Engineering at Virginia Commonwealth University. His background is in electrical and calcium sign-aling in the heart and brain. Dr Weinberg’s laboratory is focused on the development of multiscale biophysical models, with applica-tions in intracellular signaling, mechanobiol-ogy, and electrophysiology. Recent work includes studies on the role of fluctuations in intracellular calcium concentration on neu-rotransmitter release.

Jason T. Wilkes holds an MSc in mathemati-cal physics and an MA in psychology. His interests include the cognitive psychology of deductive and inductive reasoning, and the computations underlying various forms of inference and decision making under uncer-tainty. Recent work has focused on the repre-sentation of scalar magnitude and the

Notes on Contributorsxxii

computational nature of conditioning phe-nomena. His first book, Burn Math Class, was recently published by Basic Books. He is currently a graduate student in psychol-ogy at the University of California, Santa Barbara.

KongFatt Wong-Lin is a lecturer in computa-tional neuroscience at the Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Computing and Engineering, Ulster University, UK. He received his PhD in physics (computational neuroscience) from Brandeis University, followed by a research associate position at  Princeton University, affiliated with the Program in Applied and Computational Mathematics, the Center for the Study of Brain, Mind and Behavior, and Princeton Neuroscience Institute.

Si Wu is currently a professor in the State Key Laboratory of Cognitive Neuroscience and Learning and a principle investigator in the IDG/McGovern Institute for Brain Research at Beijing Normal University, China. His research interests focus on computational neuroscience and machine learning. He has published more than 100 papers, including in journals such as Neuron, Nature Neuroscience, PNAS, the Journal of Neuroscience, and NIPS. He is now serving as co-editor-in-chief for Frontiers in Computational Neuroscience.

Danke Zhang is a lecturer in the Department of Biomedical Engineering, Hangzhou Dianzi University. He received his PhD in computa-tional neuroscience from the South China University of Technology. His research inter-ests lie in neural circuit modeling under bio-physical constraints.

xxiii

I am deeply grateful to contributors of chapters in this book, as well as psycho l­ogy and neuroscience students, for fruitful

discussion on many aspects of computa­tional neuroscience.

Acknowledgment

xxv

This book provides a comprehensive collec-tion of articles covering different aspects of computational modeling efforts in psychol-ogy and neuroscience. Accordingly, this book spans different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), dif-ferent species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fit-ting, and Hodgkin–Huxley models, among others).

Computational Models of Brain and Behavior

Computational modeling studies have been increasing over the past few decades, due to the fact that we now have a large amount of experimental neuroscience data that can be better explained using a coherent framework (e.g., computational model). Computational models integrate different data sets in a coherent and unified framework in order to explain certain neuroscience phenomena. For example, single neuron models integrate data from molecular neuroscience as well as known data on neurotransmitters and their effects on neural activity. Such models can provide testable predictions at the molecular level. For example, these models may predict how changes to certain receptors affect neu-ral activity of modeled neurons.

Other models focus on simulating interac-tions among different brain regions—known as systems-level models. These models often simulate electroencephalogram (EEG), func-

tional magnetic resonance imaging (fMRI), and macro-anatomy data. These models often simulate behavior and cognition, and often provide testable predictions at the sys-tems level. For example, some of these mod-els can explain whether damage to certain brain regions can lead to cognitive deficits. Beside neural network models, other class of fitting models have been used extensively in neuroscience research. Some of these include reinforcement learning, drift diffusion, and Bayesian models. These models often have fewer parameters than network models, but they are often used to explain which param-eters explain behavior.

The book is divided into four parts: (Part 1) Models of Brain Disorders; (Part 2) Neural Models of Behavioral Pro cesses; (Part 3) Models of Brain Regions and Neu-rotransmitters, and (Part 4) Neural Modeling Approaches. Below, I summarize the chapters covered in each part.

Part 1 Models of Brain Disorders

Models of psychiatric disorders

The book includes chapters discussing mod-els of psychiatric disorders, including depres-sion, post-traumatic stress disorder (PTSD), schizophrenia, and dyslexia.

Jaffe-Dax, Raviv, Loewenstein, and Ahissar (Chapter 1) provide a computational Bayesian model of perceptual difficulties in patients with dyslexia, in which they investigated which model parameters explain perceptual

Ahmed A. Moustafa

Marcs Institute for Brain and Behaviour and School of Social Sciences and Psychology, Western Sydney University, Sydney, New South Wales, Australia

Introduction

Introductionxxvi

performance in the patients. The model sug-gested that changes to memory of past trials in relation to internal noise explain percep-tual deficits in dyslexia. This is possibly one of the few existing models that simulates behavioral performance in dyslexia. Along these lines, Tovar, Moustafa, and Arias-Trejo review existing models of atypical develop-ment, pointing to new directions to simulate behavioral and neural dysfunction in Down syndrome (Chapter 2).

Rutledge and Adams provide an overview of computational psychiatry, which is related to designing computational models to understand and perhaps help provide treat-ment for psychiatric disorders (Chapter  3). Radell, Myers, Sheynin, and Moustafa dis-cuss existing models of PTSD, focusing on the roles of the amygdala, ventromedial pre-frontal cortex, and hippocampus in memory, fear, and avoidance (Chapter  4). Chen and Takahashi discuss computational reinforce-ment learning models of depression, focus-ing on reward processes underlying its symptoms, especially anhedonia (Chapter 5). Future modeling work of depression can also lead to understanding of other symptoms of depression, including reduced mood and psychomotor retardation, which are related to dopamine dysfunction, and can thus be explained using reinforcement learning mod-els. Moustafa, Misiak, and Frydecka provide a comprehensive overview of neural network models of schizophrenia (Chapter  6). This chapter considers modeling studies of dif-ferent schizophrenia symptoms including negative and positive symptoms as well as cognitive impairment in these patients.

Models of neurological disorders

As well as psychiatric disorders, the book includes chapters that discuss models of neu-rological disorders, including Alzheimer’s dis-ease, Parkinson’s disease, and epilepsy. Two chapters here address models of Alzheimer’s disease. Skinner and Chatzikalymniou dis-cuss models of oscillation in normal and diseased states, focusing on local field poten-tials and Alzheimer’s disease (Chapter  7).

Cutsuridis and Moustafa review existing models of Alzheimer’s disease, at various levels of analysis from systems to molecular level models (Chapter 8). Two chapters here discuss Parkinson’s disease. Lowery presents modeling studies of deep brain stimulation (DBS) as a therapy for Parkinson’s disease and discusses how such models can help develop more effective stimulation systems in the future (Chapter 9). Hassan and Moustafa dis-cuss how computational models can poten-tially be used to provide better treatment for Parkinson’s disease (Chapter  10). Sanjay, Neymotin, Krothapalli, and Lytton provide an overview of models of epilepsy at differ-ent levels of analysis: cellular, molecular, systems, and behavioral neuroscience levels (Chapter  11). The chapter explains possible neural mechanisms underlying the occur-rence of partial and complex seizures. Sanjay et al. stress the importance of computational modeling work to understand how a brain disorder—here epilepsy—impacts the brain at different levels. Future work is needed to provide a unified framework to link molecu-lar changes to the occurrence of seizures in epilepsy.

Part 2 Neural Models of Behavioral Processes

The book includes chapters that focus on early sensory and perceptual processes. Zhang, Rasch, and Wu discuss models of sen-sory information processing, focusing on the dynamics of synapses and dendritic integra-tion in single neurons (Chapter  12). Joukes and Krekelberg present a neural network model of motion detection, based on data on medial temporal area (MT area) function in macaques. The model stresses the impor-tance of recurrent connections in modeling motion detection (Chapter 13).

Two chapters in the book focus on models of olfaction. While Linster provides a com-prehensive review of computational models of olfaction in rodents (Chapter 14), Gupta, Faghihi, and Moustafa summarize mod-

Introduction xxvii

els of olfaction in fruit flies (Drosophila, Chapter  15). Gupta et al. cover models of associative learning as well as the formation of olfactory memory in Drosophila. Miller and Rowland provide a model of multisen-sory integration as well as the role of the superior colliculus in such processes, includ-ing temporal and spatial aspects of sensory integration (Chapter 16).

Other chapters in the book focus on simu-lating higher cognitive and social processes. Cheong, Jolly, Sul, and Chang provide a review of existing models in social neuro-science (Chapter  17); these models include game theory and reinforcement learn-ing, and the authors explain how they help understand how the brain enables social information. Lerner provides a review of the behavioral, neural, and modeling data of sleep (Chapter 18). The chapter discusses two dominant theories that explain how sleep affects the brain and behavior: the memory reactivation theory and the syn-aptic homeostasis hypothesis. Additionally, Lerner discusses how to overcome the limi-tations of both theories and suggests a novel hypothesis that accounts for sleep, which is known as the “temporal scaffolding hypoth-esis.” As relevant to the Lerner chapter on sleep, Toutounji discusses neural models of homeostasis, focusing on how neurons and synapses keep their activity to healthy lim-its and thus allow efficient computations (Chapter 19).

Part 3 Models of Brain Regions and Neurotransmitters

Models of brain areas

The book includes chapters that focus on simulating single brain areas and neuro-transmitters, including cortex, amygdala, cerebellum, basal ganglia, and hippocampus. For example, Bahuguna and Kumar provide a computational model of the function of the striatum (main input structure of the basal ganglia), arguing that its main function

is setting a threshold for motor processes (Chapter 20).

Guntu, Feng, Alturki, Nair, Samarth, and Nair provide an overview of neurophysi-ological models of the amygdala and its role in fear learning, expression, and extinction (Chapter 21; compare to chapter by Radell et al. on PTSD, as this also discusses systems-level models of the amygdala). On the other hand, Diwakar and Moustafa review neural models of the cerebellum (as well models of cerebellum–basal ganglia interactions; Chapter 22). Although it is well known that the cerebellum plays key roles in motor pro-cesses (e.g., ataxia, motor sequencing), a wealth of data show that it also plays a role in emotional and cognitive processes, and damage to the cerebellum leads to psychiat-ric disorders, such as schizophrenia. Khalil, Moftah, Landry, and Moustafa discuss mod-els of cortical development, focusing on the following parameters: the reversal potential of GABAA, connectivity between excitatory and inhibitory neurons, and local density between neighboring neurons (Chapter 23).

Some chapters focus on simulating the function of the hippocampus. Cutsuridis pro-vides an overview of biophysically detailed microcircuit models of the hippocampus’s role in associative learning in health and dis-ease (Chapter  24). Sato summarizes models of the role of the hippocampus in episodic memory, as well as the role of theta in memory encoding and retrieval (Chapter 25). Bahrami and Farashahi provide a computational analysis for the role of the hippocampus and astrocytes in navigation, with applications to Alzheimer’s disease (Chapter  26; compare to the chapters on Alzheimer’s disease by Skinner and Chatzikalymniou and also to the other chapter by Cutsuridis and Moustafa).

Models of neurotransmitters

Some chapters discuss models of neuro-transmitters. Avery and Krichmar discuss the computational functions of different neuromodulators, including dopamine, serotonin, acetylcholine, and noradrenaline (Chapter 27). The chapter stresses the view

Introductionxxviii

that these neuromodulators do not work in isolation, and all work together to sup-port even simple behaviors, such as reward seeking or complex ones, such as cogni-tion. Avery and Krichmar argue that future models should take into consideration the interaction between these neuromodulatory systems. On the other hand, Balasubraman, Chakravarthy, Wong-Lin, Wang, Cohen, Nakamura, and Moustafa provide a review of models of serotonin (Chapter 28). The chap-ter describes data and models on the role of serotonin in decision making functions such as reward and punishment prediction, time scale of reward prediction, risk seeking or impulsivity, risk aversion, and harm avoid-ance. This chapter provides a reinforcement learning framework to explain the multiple roles of serotonin in these processes.

Part 4 Neural Modeling Approaches

This section includes chapters that focus on different methodological approaches to modeling the brain.

Higher-level models

Some of these chapters focus on higher/systems level models. Alexandre provides a comprehensive understanding of the brain, focusing on learning systems, including Pavlovian and instrumental conditioning (Chapter  29). Alexandre argued that com-putational models should take into account interactions among environment, internal/external body, and the brain. Alexandre focuses on how interactions among differ-ent learning and memory systems in the brain, including the hippocampus, basal ganglia, amygdala, cerebellum, and cortex, can allow complex behavior. Using a differ-ent approach, Güçlü and van Gerven explain how we can use artificial neural networks to understand how the brain responds to the environment, particularly focusing on rich naturalistic stimuli (Chapter 30). Nakagawa,

Adhikari, and Deco provide an overview of how models can be applied to under-stand whole-brain dynamics, focusing on large-scale models of brain structure and function (Chapter  31). Eldawlatly discusses recent algorithms used for tuning electrical stimulation for closed-loop control of neu-ral firing, with applications to epilepsy, deep brain stimulation (DBS) for Parkinson’s dis-ease, and visual prosthetics (Chapter 32; for related discussion, see the chapter by Lowery on models of DBS).

Gershman and Beck provide an overview of probabilistic inference in the brain and uncertainty of information processing, which spans perceptual, cognitive, and motor pro-cesses (Chapter  33). Ly, Boehm, Heathcote, Turner, and Forstman discuss how Bayesian hierarchical models can be used to under-stand individual differences in cognition and how to relate these to neural processes (Chapter  34). Examples are linear ballistic accumulator and drift diffusion models. This approach relies on fitting a class of models to behavioral and neural data sets. Ly et al. provide analysis of this approach to fMRI studies of basal ganglia function. The same approach can be applied to other brain areas and patient populations.

Wilkes and Gallistel provide an approach linking information theory to neurobiology (Chapter 35). Specifically, Wilkes and Gallistel discuss an associative learning model that explains cue competition and response tim-ing using maximum entropy, and minimum description length. Interestingly, Wilkes and Gallistel argue that it is important to focus not on the best model to explain the data but rather on how the data can be best com-pressed. This can possibly relate to neural processing.

Lower-level models

Some of the chapters in this book focus on lower (i.e., cellular or molecular) level models (Chapter  36). Park, Heitmann, and Ermentrout provided computational analy-sis of synchronization using phase models.