Parallel Processing in Neural Systems and Computers
Rolf Eckmiller, Georg Hartmann, Gert Hauske
North-Holland, Jan 1, 1990 - Computers - 626 pages
The 119 contributions in this book cover a range of topics, including parallel computing, parallel processing in biological neural systems, simulators for artificial neural networks, neural networks for visual and auditory pattern recognition as well as for motor control, AI, and examples of optical and molecular computing. The book may be regarded as a state-of-the-art report and at the same time as an 'Interdisciplinary Reference Source' for parallel processing. It should catalyze international and interdisciplinary cooperation among computer scientists, neuroscientists, physicists and engineers in the attempt to: 1) decipher parallel information processes in biology, physics and chemistry 2) design conceptually similar technical parallel information processors.
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activity adaptive architecture artificial neural networks associative memory back-propagation behavior Biol biological Boltzmann machine brain cells complex components Computer connectionist connections convergence correlation correlograms corresponding cortical coupling Cybern cycle Dept detectors distribution dynamics Eckmiller Elsevier Science Publishers encoding equations error example excitatory feedback Figure filter frequency function Genetic Algorithms Hartmann and G Hauske Editors hologram Hopfield IEEE implemented input layer input patterns Kohonen learning rule linear mapping matrix modulation neocognitron neural networks Neuroinformatics neurons nodes nonlinear optical optimal orientation oscillators oscillatory output units parallel parallel computer Parallel Distributed Processing parameters pattern recognition performance phase presented problem Proc processing processors Publishers B.V. North-Holland random receptive fields representation Research response robot Science Publishers B.V. self-organization sequence shows signal simulation space spatial spike stimulus structure synapses synchronization task temporal topology transputer Universitšt unsupervised learning values visual cortex weights