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Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.
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Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives by P. S. Neelakanta; Dolores DeGroff CRC Press, CRC Press LLC ISBN: 0849324882 Pub Date: 07/01/94 Search Tips
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Preface Acknowledgments Title
Chapter 1—Introduction 1.1 General -----------
1.2 Stochastical Aspects and Physics of Neural Activity 1.3 Neurocybernetic Concepts 1.4 Statistical Mechanics-Cybernetics-Neural Complex 1.5 Concluding Remarks
Chapter 2—Neural and Brain Complex 2.1 Introduction 2.2 Gross Features of the Brain and the Nervous System 2.3 Neurons and Their Characteristics 2.4 Biochemical and Electrical Activities in Neurons 2.5 Mode(s) of Communication among Neurons 2.6 Collective Response of Neurons 2.7 Neural Net: A Self-Organizing Finite Automaton 2.8 Concluding Remarks
Chapter 3—Concepts of Mathematical Neurobiology 3.1 Mathematical Neurobiology: Past and Present 3.2 Mathematics of Neural Activities 3.2.1 General considerations
3.2.2 Random sequence of neural potential spikes 3.2.3 Neural field theory 3.3 Models of Memory in Neural Networks 3.4 Net Function and Neuron Function 3.5 Concluding Remarks
Chapter 4—Pseudo-Thermodynamics of Neural Activity 4.1 Introduction 4.2 Machine Representation