Tutorial on Neural Systems Modeling
Published on 9. October 2009
Book
Hardback
542 pages
978-0-87893-339-6 (ISBN)
Description
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.
Reviews / Votes
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, 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 *More details
Person
THOMAS J. ANASTASIO is Associate Professor at the University of Illinois at Urbana-Champaign, USA, affiliated with the Department of Molecular and Integrative Physiology and the Beckman Institute for Advanced Science and Technology. 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.
Content
Vectors, Matrices, and Basic Neural Computations.- Recurrent Connections and Simple Neural Circuits.- Forward and Recurrent Lateral Inhibition.- Covariation Learning and Auto-Associative Memory.- Unsupervised Learning and Distributed Representations.- Supervised Learning and Non-Uniform Representations.- Reinforcement Learning and Associative Conditioning.- Information Transmission and Unsupervised Learning.- Probability Estimation and Supervised Learning.- Time-Series Learning and Nonlinear Signal Processing.- Temporal-Difference Learning and Reward Prediction.- Predictor-Corrector Models and Probabilistic Inference.- The Genetic Algorithm and Simulated Evolution.- Future Directions in Neural Systems Modeling.