Leon Cooper's somewhat peripatetic career has resulted in work in quantum field theory, superconductivity, the quantum theory of measurement as well as the mechanisms that underly learning and memory. He has written numerous essays on a variety of subjects as well as a highly r... egarded introduction to the ideas and methods of physics for non-physicists. Among the many accolades, he has received (some deserved) one he likes specially is the comment of an anonymous reviewer who characterized him as “a nonsense physicist”.This compilation of papers presents the evolution of his thinking on mechanisms of learning, memory storage and higher brain function. The first half proceeds from early models of memory and synaptic plasticity to a concrete theory that has been put into detailed correspondence with experiment and leads to the very current exploration of the molecular basis for learning and memory storage. The second half outlines his efforts to investigate the properties of neural network systems and to explore to what extent they can be applied to real world problems.In all this collection, hopefully, provides a coherent, no-nonsense, account of a line of research that leads to present investigations into the biological basis for learning and memory storage and the information processing and classification properties of neural systems.Contents:Some Properties of a Neural Model for MemoryA Possible Organization of Animal Memory and LearningA Theory for the Acquisition and Loss of Neuron Specificity in Visual CortexTheory for the Development of Neuron Selectivity: Orientation Specificity and Binocular Interaction in Visual Cortex Mean-Field Theory of a Neural NetworkSynaptic Plasticity in Visual Cortex: Comparison of Theory with ExperimentObjective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability ConditionsTheory of Synaptic Plasticity in Visual CortexAn Overview of Neural Networks: Early Models to Real World SystemsLearning from What's Been Learned: Supervised Learning in Multi-Neural Network Systemsand other papersReadership: Researchers and students of neural systems.Key Features:Each chapter includes a number of exercises of varying degrees of difficulty which supplement and complement the material contained in the chapterDraws upon the latest ongoing researchSelf-contained and suitable for use as a class text
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