From Neuron to Cognition via Computational Neuroscience

by Arbib, Bonaiuto

ISBN: 9780262335263 | Copyright 2016

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This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior.

The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition.

The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter).


Contributors

Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille

Michael A. Arbib

Michael Arbib has played a leading role at the interface of neuroscience and computer science ever since his first book, Brains, Machines, and MathematicsFrom Neuron to Cognition provides a worthy pedagogical sequel to his widely acclaimed Handbook of Brain Theory and Neural Networks. After thirty years at University of Southern California he is now pursuing interests in “how the brain got language” and “neuroscience for architecture” in San Diego.

James J. Bonaiuto

James J. Bonaiuto is a Postdoctoral Researcher at the Sobell Department of Motor Neuroscience and Movement Disorders at University College London.

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CONTENTS (pg. v)
SERIES FOREWORD (pg. vii)
PREFACE (pg. ix)
Who the Book Is For (pg. x)
An Overview of the Chapters (pg. xi)
HOW TO USE THIS BOOK AND ITS WEBSITE (pg. xv)
Background (pg. xv)
Materials on the Web (pg. xvi)
1 From Neuron to Cognition: An Opening Perspective (pg. 1)
1.1 Preamble (pg. 1)
1.2 The Maps Are Not the Territory (pg. 1)
1.3 Schema Theory and Action-Oriented Perception (pg. 6)
1.4 A Fast Overview of the Human Brain (pg. 9)
1.5 Neurons and Neural Networks (pg. 14)
1.6 Action (pg. 23)
1.7 Neural Plasticity, Learning and Memory (pg. 30)
1.8 Vision (pg. 38)
1.9 Bridging the Levels (pg. 47)
1.10 A Regional Focus (pg. 56)
1.11 Language (pg. 64)
1.12 An Invitation to Research (pg. 67)
2 Basic Neuron and Network Models (pg. 73)
2.1 Statistical Models of Neuronal Activity and Neural Coding (pg. 73)
2.2 Firing Rate Models (pg. 79)
2.3 Neurons (pg. 84)
2.4 Synapses (pg. 90)
2.5 Networks (pg. 92)
3 Neurons and Neuronal Networks as Dynamical Systems (pg. 101)
3.1 Neurons: Dynamical Systems of the Brain (pg. 101)
3.2 Phase Space Analysis (pg. 109)
3.3 Beyond Winner-Take-All in Network Dynamics (pg. 119)
3.4 Concluding Remarks (pg. 123)
3.5 Appendix: Basic Mathematics of Dynamical Systems (pg. 124)
4 Neural Rhythms (pg. 129)
4.1 Introduction (pg. 129)
4.2 Network Mechanisms of Rhythm Generation (pg. 132)
4.3 The Computational Role of Gamma: Larger Network Models (pg. 140)
4.4 Mechanisms and Functions of Low-Frequency Thalamocortical Rhythms (pg. 145)
4.5 Discussion (pg. 153)
5 Linking Models with Empirical Data: The Brain Operation Database (pg. 159)
5.1 The Key Components of BODB (pg. 160)
5.2 The Importance of Protocols (pg. 162)
5.3 Case Study: Models of the Mirror System (pg. 163)
5.4 Brain Imaging Data (pg. 180)
5.5 Connectivity Data (pg. 189)
5.6 Federation (pg. 190)
5.7 Collaboratory Workspaces (pg. 192)
5.8 A Sampler of Models in BODB (pg. 193)
5.9 Getting Involved with BODB (pg. 195)
6 Hebbian Learning and Plasticity (pg. 199)
6.1 Introduction to Synaptic Plasticity and Long-Term Potentiation (pg. 199)
6.2 Hebbian Learning (pg. 202)
6.3 Functional Consequences of Hebbian Learning (pg. 206)
6.4 Spike Timing Dependent Plasticity (pg. 210)
6.5 Reward-Modulated Learning (pg. 214)
6.6 Core Set of Readings (pg. 216)
7 Neuromodulation (pg. 219)
7.1 Neuromodulation: What Is It? (pg. 219)
7.2 Extrinsic and Intrinsic Neuromodulations (pg. 220)
7.3 Time Course of Neuromodulation (pg. 221)
7.4 Dopaminergic Modulation (pg. 222)
7.5 Cholinergic Modulation (pg. 233)
7.6 Conclusions (pg. 242)
8 Motor Pattern Generation (pg. 251)
8.1 Pattern Generation and Motor Control (pg. 251)
8.2 Modeling Pattern Generators (pg. 260)
8.3 Pattern Generators and Robotics (pg. 273)
8.4 Conclusion (pg. 275)
9 Computational Models in Motor Control (pg. 285)
9.1 Introduction: The Motor Control Problem (pg. 285)
9.2 The Complex Motor System (pg. 285)
9.3 Feedback and Feedforward Control (pg. 286)
9.4 Internal Models: Forward and Inverse (pg. 289)
9.5 State Estimation (pg. 292)
9.6 Optimal Control (pg. 293)
9.7 Motor Learning (pg. 295)
9.8 Conclusions (pg. 296)
10 Reinforcement Learning (pg. 299)
10.1 Learning Behavior: Classical Conditioning and Prediction Errors (pg. 299)
10.2 Dopamine and Reinforcement Learning (pg. 301)
10.3 Sequential Predictions and TemporalDifference Learning (pg. 306)
10.4 Dopamine and Sequential Choice (pg. 309)
10.5 Conclusion and Arbitration (pg. 314)
11 Short-Term, Long-Term, and Working Memory (pg. 319)
11.1 General Definitions and Background (pg. 319)
11.2 Neural and Synaptic Mechanisms Underlying Memory (pg. 320)
11.3 Models of Shortand Long-Term Memory (pg. 323)
11.4 Models of Working Memory (pg. 330)
11.5 Conclusions (pg. 340)
12 Early Vision (pg. 345)
12.1 Basic Concepts and Overview (pg. 345)
12.2 Linear and Complex Filters (pg. 356)
12.3 Probabilities and Decision Theory (pg. 371)
12.4 Context and Spatial Interactions between Neurons (pg. 378)
12.5 Cue Coupling (pg. 390)
12.6 Summary and the Relations of Early and High-Level Vision (pg. 401)
13 Neural Maps: Their Function and Development (pg. 409)
13.1 Overview (pg. 409)
13.2 Biological Background (pg. 409)
13.3 Questions about Neural Maps (pg. 412)
13.4 Where Do Neural Map Patterns Come From? (pg. 413)
13.5 How Do Feature Maps Arise from Neural Mechanisms? (pg. 418)
13.6 What Is the Information-Processing Goal of Neurons in Maps? (pg. 422)
13.7 Nonvisual Maps (pg. 427)
13.8 Concluding Discussion (pg. 428)
13.9 Summary (pg. 429)
14 Schema Theory and Neuropsychology (pg. 433)
14.1 Introduction (pg. 433)
14.2 Theoretical Background (pg. 434)
14.3 Perceptual and Motor Schemas in the Frog and Toad (pg. 435)
14.4 Higher-Level Schemas for the Control of Thought and Action in Humans (pg. 437)
14.5 The Interactive Activation and Competition Model of Contention Scheduling (pg. 441)
14.6 The Simple Recurrent Network Model of Routine Sequential Action Selection (pg. 447)
14.7 Modeling Neurological Deficits (pg. 452)
14.8 Concluding Discussion (pg. 454)
15 Synthetic Brain Imaging (pg. 457)
15.1 Introduction to Neuroimaging Methods and Issues Specific to Computational Modeling of Human Imaging Data (pg. 457)
15.2 Models Linking PET/fMRI Data to Neurophysiological Data: Animal Data and Neuronal Dynamics (pg. 468)
15.3 Models Linking PET/fMRI Data to Neuropsychological Data: Behavioral and Cognitive States (pg. 473)
15.4 Synthetic Imaging of Disease, Connectivity, and Data-Driven Learning (pg. 476)
15.5 Chapter Summary (pg. 479)
16 Embodied Models and Neurorobotics (pg. 483)
16.1 Physical Models in Neurobiology (pg. 483)
16.2 Embodiment Matters (pg. 484)
16.3 Embodied Models as Means to Understand Animal Sensorimotor and Information-Processing Capabilities (pg. 489)
16.4 Neurorobotics of the Mammalian Vibrissal System (pg. 496)
16.5 Scaling Up to the Whole Brain (pg. 501)
16.6 Conclusion—Advancing Brain Theory through Neurorobotics (pg. 505)
Acknowledgments (pg. 507)
17 Object and Scene Perception (pg. 513)
17.1 Introduction (pg. 513)
17.2 Visual Cortex (pg. 513)
17.3 Feedforward Models (pg. 516)
17.4 Feedforward Scene Recognition (pg. 522)
17.5 Feedback Connections (pg. 524)
17.6 Conclusion (pg. 528)
18 Hippocampus: From Spatial Processing to Episodic Memory (pg. 533)
18.1 Overview and Summary (pg. 533)
18.2 Structure and Function of the Hippocampal System (pg. 533)
18.3 A Theory of the Operation of Hippocampal Circuitry as a Memory System (pg. 536)
18.4 Navigation and the Hippocampus (pg. 550)
18.5 Tests of the Theory (pg. 551)
18.6 Conclusion (pg. 552)
Acknowledgments (pg. 553)
19 Saccades and Smooth Pursuit Eye Movements (pg. 559)
19.1 Introduction (pg. 559)
19.2 Saccades (pg. 560)
19.3 Smooth Pursuit (pg. 572)
19.4 Fixational Eye Movements (pg. 578)
19.5 Conclusion (pg. 580)
20 Reach and Grasp: Control, Development, and Recognition (pg. 585)
20.1 Neural Control of Arm and Hand Movements (pg. 586)
20.2 Reaching (pg. 589)
20.3 Grasping (pg. 592)
20.4 Reach and Grasp Coordination (pg. 607)
20.5 Action Recognition and the Mirror System (pg. 608)
20.6 Conclusion (pg. 614)
21 Cerebellar Adaptation and Supervised Learning in Motor Control (pg. 617)
21.1 Basic Concepts: The Purkinje Cell as a Signal Synthesis Device (pg. 617)
21.2 The Learning Rule: Application to Noise Cancellation (pg. 624)
21.3 The Adaptive Filter: Application to Motor Control (pg. 629)
21.4 A Framework for Validating Cerebellar Models (pg. 637)
21.5 General Conclusions (pg. 644)
22 Integrative Functions of the Corticostriatal System (pg. 649)
22.1 Functional Overview of the Basal Ganglia Corticostriatal System Illustrated with the Oculomotor Saccade System (pg. 650)
22.2 Modeling Plasticity and Integrative Functions of the Corticostriatal System (pg. 656)
22.3 Dopamine Function and System-Level Models of Corticostriatal System (pg. 666)
22.4 Future Challenges (pg. 668)
23 Brain Diseases (pg. 673)
23.1 Epilepsy (pg. 673)
23.2 Parkinson’s Disease (pg. 678)
23.3 Stroke (pg. 683)
23.4 Schizophrenia (pg. 687)
23.5 Summary and Conclusions (pg. 689)
24 Language Processing (pg. 693)
24.1 Language and the Brain (pg. 693)
24.2 Neural Network Models of Language Learning (pg. 696)
24.3 Language, Brain and Body (pg. 707)
24.4 Conclusions (pg. 713)
25 Evolving the Language-Ready Brain (pg. 719)
25.1 Introduction (pg. 719)
25.2 The Mirror System Hypothesis on the Evolution of the Language-Ready Brain (pg. 724)
25.3 Modeling the Macaque Brain (pg. 730)
25.4 Grounding Ape Gestural Development in Macaque Brain Models (pg. 734)
25.5 MSH and the Brain Mechanisms Underlying Language (pg. 740)
25.6 Template Construction Grammar: Linking Language and Vision (pg. 746)
25.7 Challenges for Further Research (pg. 753)
Appendix A: Basics of Connectionist Networks (pg. 759)
A.1 Connectionist Networks as Dynamical Systems (pg. 759)
A.2 Hopfield Nets (pg. 760)
A.3 Learning in Connectionist Networks (pg. 761)
A.4 Attractor Networks (pg. 764)
Appendix B: Autoassociation, Pattern Association, and Competitive Networks (pg. 767)
B.1 Summary of the Architecture and Operation of Autoassociation or Attractor Networks (pg. 767)
B.2 Summary of the Architecture and Operation of Pattern Association Networks (pg. 768)
B.3 Summary of the Architecture and Operation of Competitive Networks (pg. 768)
Contributors (pg. 771)
INDEX (pg. 773)
Computational Neuroscience (pg. 791)
Student
Chapter 1 - From Neuron to Cognition: An Opening Perspective
Last Updated: Nov 1 2016
Contains: Lecture slides
Chapter 2 - Basic Neuron and Network Models
Last Updated: Nov 1 2016
Contains: Lecture slides and Exercises/Projects
Chapter 3 - Neurons and Neuronal Networks as Dynamical Systems
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 4 - Neural Rhythms
Last Updated: Nov 9 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 5 - Linking Models with Empirical Data: The Brain Operation Database
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

The Brain Operation Database

BODB Entries
Chapter 6 - Hebbian Learning and Plasticity
Last Updated: Nov 1 2016
Contains: Lecture slides and Exercises/Projects
Chapter 7 – Neuromodulation
Last Updated: Nov 1 2016
Contains: Lecture slides, Exercises/Projects, and Extra files/Links
Chapter 8 - Motor Pattern Generation
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 9 - Computational Models in Motor Control
Last Updated: Nov 1 2016
Contains: Lecture slides, Exercises/Projects, and Extra files/Links
Chapter 10 - Reinforcement Learning
Last Updated: Nov 1 2016
Contains: Lecture slides and Exercises/Projects
Chapter 11 - Short-Term, Long-Term, and Working Memory
Last Updated: Nov 1 2016
Contains: Lecture slides and Exercises/Projects
Chapter 12 - Early Vision
Last Updated: Nov 1 2016
Contains: Lecture slides, Exercises/Projects, and Extra files/Links
Chapter 13 - Neural Maps: Their Function and Development
Last Updated: Nov 1 2016
Contains: Lecture slides
Chapter 14 - Schema Theory and Neuropsychology
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 15 - Synthetic Brain Imaging
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, and Extra files/Links

BODB Entries
Chapter 16 - Embodied Models and Neurorobotics
Last Updated: Nov 17 2016
Contains: Lecture slides
Chapter 17 - Object and Scene Perception
Last Updated: Nov 1 2016
Contains: Lecture slides, Exercises/Projects, and Extra files/Links
Chapter 18 - Hippocampus: From Spatial Processing to Episodic Memory
Last Updated: Nov 1 2016

Contains: Lecture slides, Exercises/Projects, and Extra files/Links

Chapter 19 - Saccades and Smooth Pursuit Eye Movements
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 20 - Reach and Grasp: Control, Development, and Recognition
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 21 - Cerebellar Adaptation and Supervised Learning in Motor Control
Last Updated: Nov 1 2016
Contains: Lecture slides, Exercises/Projects, and Extra files/Links
Chapter 22 - Integrative Functions of the Corticostriatal System
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, and Extra files/Links

BODB Entries
Chapter 23 - Brain Diseases
Last Updated: Nov 1 2016
Contains: Lecture slides, BODB Entries, Exercises/Projects, and Extra files/Links

BODB Entries
Chapter 24 - Language Processing
Last Updated: Nov 9 2016
Chapter 25 - Evolving the Language-Ready Brain
Last Updated: Nov 1 2016
Contains: Lecture slides and Exercises/Projects
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