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Models of Neural Networks
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the ...
23 Problems in Systems Neuroscience
The complexity of the brain and the protean nature of behavior remain the most elusive area of science, but also the most important. van Hemmen and Sejnowski invited 23 experts from the many areas--from evolution to qualia--of systems neuroscience to formulate one problem each. Although each chapter was written independently and can be read separately, together they provide a useful roadmap to the field of systems neuroscience and will serve as a source of inspirations for future explorers of the brain.
Models of Neural Networks II
Provides an in-depth analysis of both paradigms starting with an introduction to the ideas used in the subsequent chapters. In this book, one finds a discussion of salient features such as coherent oscillations and their detection, associative binding and segregation, Hebbian learning, and sensory computations in the visual and olfactory cortex.
Neural Networks
The mysteries of the human mind have fascinated scientists and philosophers alike for centuries. Descartes identified our ability to think as the foundation stone of ontological philosophy. Others have taken the human mind as evidence of the existence of supernatural powers, or even of God. Serious scientific in vestigation, which began about half a century ago, has partially answered some of the simpler questions (such as how the brain processes visual information), but has barely touched upon the deeper ones concerned with the nature of consciousness and the possible existence of mental features transcending the biological substance of the brain, often encapsulated in the concept "soul". B...
Current Catalog
First multi-year cumulation covers six years: 1965-70.
Models of Neural Networks III
One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.
The Cumulative Book Index
A world list of books in the English language.
