Introduction to neural computation. Biological neurons. Fundamental concepts
behind various models of neural networks. Functional equivalence and conver-
gence properties of neural network models. Adaptation and learning in neural
networks: associative, competitive, inhibitory, and adaptive resonance models
of learning. Back-propagation, Hopfield Nets, Boltzmann machines, Cauchy ma-
chines, ART, and feature map (Kohonen model). Cognitron and neocognitron.
VLSI, optical, and software implementations. Potentials and limitations of neu-
ral networks. Applications to vision, speech, motor control and others. Projects.​