Tutorial on Neural Systems Modeling

ISBN : 9780878933396

Thomas J. Anastasio
542 Pages
216 x 276 mm
Pub date
Oct 2009
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For students of neuroscience and cognitive science who wish to explore the functioning of the brain further, but lack an extensive background in computer programming or maths, this new book makes neural systems modelling truly accessible. Short, simple MATLAB computer programs give readers all the experience necessary to run their own simulations.


1. Vectors, Matrices, and Basic Neural Computations
2. Recurrent Connections and Simple Neural Circuits
3. Forward and Recurrent Lateral Inhibition
4. Covariation Learning and Auto-Associative Memory
5. Unsupervised Learning and Distributed Representations
6. Supervised Learning and Non-Uniform Representations
7. Reinforcement Learning and Associative Conditioning
8. Information Transmission and Unsupervised Learning
9. Probability Estimation and Supervised Learning
10. Time-Series Learning and Nonlinear Signal Processing
11. Temporal-Difference Learning and Reward Prediction
12. Predictor-Corrector Models and Probabilistic Inference
13. Simulated Evolution and the Genetic Algorithm
14. Future Directions in Neural Systems Modeling

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.

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