An Introduction to Neural and Electronic Networks

This is a presentation of research and theory from the disciplines that provide the foundations of neural network research: neurobiology, physics, computer science, electrical engineering, mathematics and psychology. It shows how neural networks and neurocomputing represent radical departures from conventional approaches to digital computers, in terms of algorithms as well as architecture. More than 200 line drawings illustrate the many facets of and approaches to neural networks research. This second edition contains new chapters on computational models of hippocampal and cerebellar function, nonlinear information processing, adaptive filtering and pattern recognition, and digital VLSI architecture. Its interdisciplinary emphasis is aimed at a wide array of researchers and students - from neurobiologists to psychologists.

[1]  S. Kelso,et al.  Hebbian synapses in hippocampus. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[2]  R. Malinow,et al.  Postsynaptic hyperpolarization during conditioning reversibly blocks induction of long-term potentiation , 1986, Nature.

[3]  T. Sejnowski.  Statistical constraints on synaptic plasticity. , 1977, Journal of theoretical biology.

[4]  Terrence J. Sejnowski,et al.  Building network learning algorithms from Hebbian synapses , 1990 .

[5]  B. McNaughton,et al.  Synaptic enhancement in fascia dentata: Cooperativity among coactive afferents , 1978, Brain Research.

[6]  T. Sejnowski,et al.  Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.

[7]  S. Sherman,et al.  Organization of visual pathways in normal and visually deprived cats. , 1982, Physiological reviews.

[8]  James L. McGaugh,et al.  Brain Organization and Memory: Cells, Systems, and Circuits , 1992 .

[9]  L. Finkel,et al.  Interaction of synaptic modification rules within populations of neurons. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[10]  J.A. Anderson.  Two models for memory organization using interacting traces , 1970 .

[11]  R Linsker,et al.  From basic network principles to neural architecture: emergence of spatial-opponent cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[12]  D. Hubel,et al.  Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. , 1965, Journal of neurophysiology.

[13]  T. Sejnowski,et al.  Associative long-term depression in the hippocampus induced by hebbian covariance , 1989, Nature.

[14]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[15]  D. GABOR,et al.  Holographic Model of Temporal Recall , 1968, Nature.

[16]  B. Gustafsson,et al.  Long-term potentiation in the hippocampus using depolarizing current pulses as the conditioning stimulus to single volley synaptic potentials , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  W. Singer,et al.  Pharmacological induction of use-dependent receptive field modifications in the visual cortex. , 1988, Science.

[18]  Tj Sejnowski,et al.  Skeleton filters in the brain , 2014 .

[19]  G. Lynch,et al.  Heterosynaptic depression: a postsynaptic correlate of long-term potentiation , 1977, Nature.

[20]  H. C. Klonguet-Higgins,et al.  Holographic model of temporal recall. , 1968, Nature.

[21]  G. Lynch,et al.  Induction of synaptic potentiation in hippocampus by patterned stimulation involves two events. , 1986, Science.

[22]  W. Levy,et al.  Synapses as associative memory elements in the hippocampal formation , 1979, Brain Research.

[23]  Idan Segev,et al.  Signal enhancement in distal cortical dendrites by means of interactions between active dendritic spines. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Y. Frégnac,et al.  A cellular analogue of visual cortical plasticity , 1988, Nature.

[25]  G. Stent.  A physiological mechanism for Hebb's postulate of learning. , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Roger D. Traub.  Methods in Neuronal Modeling: From Synapses to Networks. Based on a Course Held in Woods Hole, August 1988. Christof Koch , Idan Segev , 1990 .

[27]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation columns. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[28]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[29]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[30]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[31]  A. Klopf.  A neuronal model of classical conditioning , 1988 .

[32]  T. H. Brown,et al.  Associative long-term potentiation in hippocampal slices. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[33]  L. Cooper,et al.  A physiological basis for a theory of synapse modification. , 1987, Science.

[34]  G. Collingridge,et al.  Excitatory amino acids in synaptic transmission in the Schaffer collateral‐commissural pathway of the rat hippocampus. , 1983, The Journal of physiology.

[35]  H. Wigström,et al.  A possible correlate of the postsynaptic condition for long-lasting potentiation in the guinea pig hippocampus in vitro , 1984, Neuroscience Letters.

[36]  W. Levy,et al.  Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus , 1983, Neuroscience.