Description
Book SynopsisThis book provides a selection of papers of the late Daniel Amit (1938-2007). Daniel Amit was a physicist who spent the last 22 years of his life working on neural network models. He was one of the pioneers in the field. The volume contains 21 papers, from the highly influential 1985 paper on the Hopfield model (published together with Hanoch Gutfreund and Haim Sompolinsky), to his last (unpublished) manuscript. Many of these papers are landmark papers in the field. The book also provides a biography; an introduction on Daniel Amit's scientific career before the Hopfield model; and introductions to each of the included papers, written by their co-authors. This book will be of interest to physicists, computational neuroscientists and neurobiologists.
Table of ContentsIntroduction; The Hopfield Model; A Network Counting Chimes; Associative Memory Networks at Low Rates; Towards Networks of Spiking Neurons; The Miyashita Correlations; Learning in Networks with Discrete Synapses; The BBS Review; Dynamics of Networks of Spiking Neurons; Electronic Implementations; Prospective Activity; Multi-Item Working Memory; Learning with Spike-Driven Plastic Synapses; Familiarity Recognition; Unpublished Manuscript.
