Cognizers: Neural Networks and Machines that Think
One of the most dramatic developments in computer science has been the effort to create machines that duplicate the neurotransmitter biology of the human brain. Describing for the general reader how human neural networks work, the authors explain how this cutting-edge technology could be the breakthrough that makes artificial intelligence a reality. The approach combines history and hard science with exhaustive research, all presented in an engaging, lively writing style.
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Computer Science Needs
Seminal Turing Machines Von Neumann
Computers Today All Is Not Lost Learning
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ability activity adaptive resonance analog artificial intelligence artificial neurons AT&T automata axon basic behavior biological Boltzmann machine brain build built called causal cells chemical chips circuit circuitry cognizers complex connectionist connections cortex Descartes developed devices digital computer electrical electronic encoded energy engineering environment experience expert systems feedback firing formal system function Grossberg Hecht-Nielsen Hopfield human input interconnections John Hopfield Laboratories layer logical long-term memory mathematical Mead mechanisms membrane microchips mind nerves nervous system Neumann neural net Neural Nets neural networks Neurocomputer nodes objects operations optical organs output outstar patterns perceptions perceptron perform physical problem processor puter raw sensation recognize reductionist researchers result Robert Hecht-Nielsen Rosenblatt sampling signal scientists Sejnowski sensory short-term memory signal silicon simple simulation simultaneously solve specific Stephen Grossberg stored synaptic weights theorem theory thought threshold tion transistors Turing machine units University Widrow wires