Neural nets and chaotic carriers
著者
書誌事項
Neural nets and chaotic carriers
（Wiley-Interscience series in systems and optimization）
Wiley, 1998
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注記
Bibliography: p. [197]-201
Includes index
内容説明・目次
内容説明
Neural Nets and Chaotic Carriers is an innovatory text, in that it develops rational principles for the design of associative memories with a view to applying these principles to models with the irregularly oscillatory operation so evident in biological neural systems. It thus bridges studies of artificial and of biological neural networks, with new results for both. The text has a strong research character, but a concise exposition from the basics makes it accessible to non-specialists. Design is based on the criterion that an associative memory must be able to cope with 'fading data', ie to form an inference from data even as its memory of that data degrades. The resultant net shows striking biological parallels, suggesting testable anatomical predictions. Many questions concerning composite or 'spurious' traces and memory capacity are clarified. The approach taken to models of the biological neuron and oscillation in systems of such neurons follows the pioneering ideas of W. J. Freeman, and develops these. In particular, when the associative memory principles are combined with oscillatory operation, some remarkable effects emerge.
For example, the system shows a low-frequency square-wave oscillation (the 'escapement oscillation') with gamma-range bursts at its peaks, much as is observed in electroencephalograms. The text will be invaluable for researchers and graduate workers with a primary interest in artificial of biological neural nets. However, it is also accessible and interesting to anyone with the mathematical background usual in artificial intelligence, computer science, systems studies or statistics, for example.
目次
OPENING AND THEMES. Introduction and Aspirations. Optimal Statistical Procedures. Linear Links and Nonlinear Knots: The Basic Neural Net. Bifurcations and Chaos. ASSOCIATIVE AND STORAGE MEMORIES. What is a Memory? The Hamming and Hopfield Nets. Compound and 'Spurious' Traces. Preserving Plasticity: A Bayesian Approach. The Key Task: The Fixing of Fading Data. Conclusions I. Performance of the Probability-Maximising Algorithm. Other Memories--Other Considerations. OSCILLATORY OPERATION AND THE BIOLOGICAL MODEL. Neuron Models and Neural Masses. Freeman Oscillators--Solo and in Concert. Associative Memories Incorporating the Freeman Oscillator. Olfactory Comparisons. Conclusions II. Transmission Delays. Appendix. References. Index.
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