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Informationen zum Autor 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. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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. ...
