Neural Networks for Perception: Computation, Learning and Architecture v. 2

Computation, learning and architectures: learning visual behaviours, D.Ballard and S.Whitehead; non-parametric regression analysis using self-organizing toplological maps, V.Cherkassy and H.Lari-Najafi; theory of the backpropagation neural network, non-parametric regression analysis using self-organizing topological maps; Hopfield model and optimization problems, B.Kamgar-Parsi and B.Kamgar-Parsi; DAM regression analysis and attentive recognition, W.Polzleitner; intelligent code machine, V.Stern; cycling logarithmically convergent networks that flow information to behave (perceive) and learn, L. Uhr; computation and learning in the context of neural network capacity, S.Venkatesh; competitive and cooperative multimode dynamics in photorefractive ring circuits, D.Anderson et al; hybrid neural networks and algorithms, D.Casasent; the use of fixed holograms for massively-interconnected, low-power neural networks, H.Jeon et al; electronic circuits for adaptive synapses, J.Mann and J.Raffel; neural network computations on a fine grain array processor, S.J.Wilson.