Tutorial on Neural Systems Modeling

Thomas J. Anastasio

October 9, 2009
542 pages
casebound

About This Title

Neural systems models are elegant conceptual tools that provide satisfying insight into brain function. The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer programming.

The book opens with an introduction to computer programming. Each of twelve subsequent chapters presents a different modeling paradigm by describing its basic structure and showing how it can be applied in understanding brain function. The text guides the reader through short, simple computer programs—printed in the book and available by download at the companion website—that implement the paradigms and simulate real neural systems. Motivation for the simulations is provided in the form of a narrative that places specific aspects of neural system behavior in the context of more general brain function. The narrative integrates instruction for using the programs with description of neural system function, and readers can actively experience the fun and excitement of doing the simulations themselves. Designed as a hands-on tutorial for students, this book also serves instructors as both a teaching tool and a source of examples and exercises that provide convenient starting points for more in-depth exploration of topics of their own specific interest.

The distinguishing pedagogical feature of this book is its computer programs, written in MATLAB, that help readers develop basic skill in the area of neural systems modeling. (All of the program files are available online via the book’s companion website: www.sinauer.com/anastasio.) Actual data on real neural systems is presented in the book for comparison with the results of the simulations. Also included are asides (“Math Boxes”) that present mathematical material that is relevant but not essential to running the programs. Exercises and references at the end of each chapter invite readers to explore each topic area on their own.

About the Author

Thomas J. Anastasio is Associate Professor at the University of Illinois at Urbana–Champaign, affiliated with the Department of Molecular and Integrative Physiology and the Beckman Institute for Advanced Science and Technology. He earned a B.S. in Psychology at McGill University, and a Ph.D. in Physiology and Biophysics from the University of Texas at Galveston. A teacher of courses in computational neuroscience for nearly two decades, Dr. Anastasio has received the James E. Heath Award for Excellence in Teaching Physiology at the University of Illinois. His research focuses on the computational modeling of the nervous system in health and disease.

Reviews and Commentary

“The enthusiasm expressed in the book is infectious. The writing is exceedingly clear and the concepts well expressed.”
—Jay McClelland, Stanford University

“I am very impressed. Tom Anastasio has created something that has been needed for a long time—a textbook that makes the relevant aspects of neural networks accessible to neuroscience students, whose mathematics preparation may be limited. I like the way he has interwoven the theory, the math, the computer simulations, and the neurobiology.”
—David Zipser, Emeritus, University of California, San Diego

“I like the level and style of presentation a lot. The MATLAB link is a huge plus, and one that makes all the computations come to life.”
—Shihab Shamma, University of Maryland at College Park

“The author has done a great job of bringing a variety of models under one umbrella and going over them in detail. I like the fact that there is MATLAB code for hands-on learning. The mathematical details are also clearly explained. Students should have no problem understanding how these models work.”
—Rajesh P. N. Rao, University of Washington, Seattle

“The writing is extremely clear, and the author conveys pretty advanced ideas very well.”
—Maxim Raginsky, Duke University

1. Vectors, Matrices, and Basic Neural Computations
The brain is the most complex organ known to exist, yet simple mathematical and computer programming methods can be used to simulate many neural systems.

Neural Systems, Neural Networks, and Brain Function
Using MATLAB: The Matrix Laboratory Programming Environment
Imitating the Solution by Gauss to the Busywork Problem
Operation and Habituation of the Gill Withdrawal Reflex of Aplysia
The Dynamics of a Single Neural Unit with Positive Feedback
Neural Networks: Neural Systems with Multiple Interconnected Units

2. Recurrent Connections and Simple Neural Circuits
Small networks with recurrent connections, forming circuits, can shape signals in time, produce oscillations, and simulate neural systems involved in low-level motor control.

The Dynamics of Two Neural Units with Feedback in Series
Signal Processing in the Vestibulo-Ocular Reflex (VOR)
The Parallel Pathway Model of Velocity Storage in the Primate VOR
The Positive Feedback Model of Velocity Storage in the Primate VOR
The Negative Feedback Model of Velocity Leakage in the Pigeon VOR
Oculomotor Neural Integration via Reciprocal Inhibition
Simulating the Insect Flight Central Pattern Generator

3. Forward and Recurrent Lateral Inhibition
Networks with forward and recurrent laterally inhibitory connectivity profiles can shape signals in space and time and simulate certain forms of sensory and motor processing.

Simulating Edge Detection in the Early Visual System of Limulus
Simulating Center/Surround Receptive Fields Using the Difference of Gaussians
Simulating Activity Bubbles and Stable Pattern Formation
Separating Signals from Noise and Modeling Target Selection in the Superior Colliculus

4. Covariation Learning and Auto-Associative Memory
Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall those patterns and simulate certain forms of memory.

The Four Hebbian Learning Rules for Neural Networks
Simulating Memory Recall Using Recurrent Auto-Associator Networks
Recalling Distinct Memories Using Negative Connections in Auto-Associators
Synchronous versus Asynchronous Updating in Recurrent Auto-Associators
Graceful Degradation and Simulated Forgetting
Simulating Storage and Recall of a Sequence of Patterns
Hebbian Learning, Recurrent Auto-Association, and Models of Hippocampus

5. Unsupervised Learning and Distributed Representations
Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.

Learning through Competition to Specialize for Specific Inputs
Training Few Output Neurons to Represent Many Input Patterns
Simulating the Formation of Brain Maps using Cooperative Mechanisms
Modeling the Formation of Tonotopic Maps in the Auditory System
Simulating the Development of Orientation Selectivity in Visual Cortex
Modeling a Possible Multisensory Map in the Superior Colliculus

6. Supervised Learning and Non-Uniform Representations
Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

Using the Classic Hebb Rule to Learn a Simple Labeled Line Response
Learning a Simple Contingency Using the Covariation Rule
Using the Delta rule to Learn a Complex Contingency
Learning Interneuronal Representations using Back-Propagation
Simulating Catastrophic Retroactive Interference in Learning
Simulating the Development of Non-Uniform Distributed Representations
Modeling Non-Uniform Distributed Representations in the Vestibular Nuclei

7. Reinforcement Learning and Associative Conditioning
Reinforcement learning algorithms can simulate certain forms of associative conditioning and can train networks to develop non-uniform distributed representations.

Learning the Labeled-Line Task via Perturbation of One Weight at a Time
Perturbing All Weights Simultaneously and the Importance of Structure
Plausible Weight Modification using Perturbative Reinforcement Learning
Reinforcement Learning and Non-Uniform Distributed Representations
Reinforcement in a Schema Model of Avoidance Conditioning
Exploration and Exploitation in a Model of Avoidance Conditioning

8. Information Transmission and Unsupervised Learning
Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about the input and simulate the properties of sensory neurons.

Some Basic Concepts in Information Theory
Measuring Information Transmission through a Neural Network
Maximizing Information Transmission in a Neural Network
Information Transmission and Competitive Learning in Neural Networks
Information Transmission in Self-Organized Map Networks
Information Transmission in Stochastic Neural Networks

9. Probability Estimation and Supervised Learning
Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation.

Implementing a Simple Classifier as a Three-Layered Neural Network
Predicting Rain as an Everyday Example of Probabilistic Inference
Implementing a Simple Classifier Using Bayes’ Rule
Modeling Neural Responses to Sensory Input as Probabilistic Inference
Modeling Multisensory Collicular Neurons as Probability Estimators

10. Time-Series Learning and Nonlinear Signal Processing
Supervised learning through time can train neural networks to produce dynamic transformations and simulate certain forms of motor control and short-term memory.

Training Connection Weights in Nonlinear Recurrent Neural Networks
Training a Two-unit Network to Simulate the Oculomotor Neural Integrator
Velocity Storage in the Primate Vestibulo-Ocular Reflex
Training a Network of Linear Units to produce Velocity Storage
Training Networks of Nonlinear Units to Produce Velocity Storage
Training a Recurrent Neural Network to Simulate Short-Term Memory

11. Temporal-Difference Learning and Reward Prediction
Temporal-difference learning can train neural networks to estimate the future value of a current state and simulate the responses of neurons involved in reward processing.

Learning State Values using Iterative Dynamic Programming
Learning State Values using Least Mean Squares
Learning State Values using the Method of Temporal Differences
Simulating Dopamine Neuron Responses using Temporal Difference Learning

12. Predictor-Corrector Models and Probabilistic Inference
Predictor-corrector models can improve perception by combining internal expectations with sensory observations and simulate the responses of certain sensory neurons.

Modeling Visual System Direction Selectivity using Asymmetric Inhibition
Modeling Visual Processing as Bottom-Up/Top-Down Probabilistic Inference
A Predictor-Corrector Model of Predictive Tracking by Midbrain Neurons
Training a Sigmoidal Unit to Simulate Trajectory Prediction by Neurons

13. The Genetic Algorithm and Simulated Evolution
The genetic algorithm simulates the process of evolution and can be used to optimize the structure, connectivity, and adaptability of neural systems.

Simulating Genes and Genetic Operators
Exploring a Simple Example of Simulated Genetic Evolution
Evolving the Sizes of Neural Networks to Improve Learning
Evolving Optimal Learning Rules for Auto-Associative Memories
Evolving Connectivity Profiles for Activity-Bubble Neural Networks

14. Future Directions in Neural Systems Modeling
In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within units to interactions between networks.

Neuro-Informatics and Molecular Networks
Enhanced Learning in Neural Networks with Smart Synapses
Combining Complementary Network Paradigms for Memory Formation
Smart Synapses and Complementary Rules in Cerebellar Learning
A Final Word

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Tutorial on Neural Systems Modeling	978-0-87893-339-6	$74.95	Purchase	Request Exam Copy
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