edited by Leon N Cooper (Brown University)

Table of Contents (146k)
General Introduction (52k)
Part I. Physiological Basis of Learning and Memory Storage (533k)

About Leon N Cooper

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 regarded 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.

• Some Properties of a Neural Model for Memory
• A Possible Organization of Animal Memory and Learning
• A Theory for the Acquisition and Loss of Neuron Specificity in Visual Cortex
• Theory for the Development of Neuron Selectivity: Orientation Specificity and Binocular Interaction in Visual Cortex
• Mean-Field Theory of a Neural Network
• Synaptic Plasticity in Visual Cortex: Comparison of Theory with Experiment
• Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions
• Theory of Synaptic Plasticity in Visual Cortex
• An Overview of Neural Networks: Early Models to Real World Systems
• Learning from What's Been Learned: Supervised Learning in Multi-Neural Network Systems
• and other papers