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Fundamentals of Neural Network Modeling
Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes.Over the past few years, computer modeling has become more prevalent …
Visual Cortex and Deep Networks
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications.The ventral visual …
Circuit Complexity and Neural Networks
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and …
Knowledge-Based Neurocomputing
Looking at ways to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.Neurocomputing methods are loosely based on a model of the …
An Introduction to Computational Learning Theory
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students …