Statistical Mechanics of Neural Networks: Proceedings of the XIth Sitges Conference Sitges, Barcelona, Spain, 37 June 1990 Softcover reprint of the original 1st ed. 1990 [Pehme köide]

On the statistical-mechanical formulation of neural networks.- Model
neurons: From Hodgkin-Huxley to hopfield.- Statistical mechanics for networks
of analog neurons.- Properties of neural networks with multi-state neurons.-
Adaptive recurrent neural networks and dynamic stability.- Neuronal
oscillators: Experiments and models.- Neuronal networks in the hippocampus
involved in memory.- Basins of attraction and spurious states in neural
networks.- Tailoring the performance of attractor neural networks.- Learning
and optimization.- Statistical dynamics of learning.- Learning and retrieving
marked patterns.- Learning algorithm for binary synapses.- Statistical
mechanics of the perceptron with maximal stability.- Simulation and hardware
implementation of competitive learning neural networks.- Learning in
multilayer networks: A geometric computational approach.- Storage capacity of
diluted neural networks.- Dynamics and storage capacity of neural networks
with sign-constrained weights.- The neural basis of the locomotion of
nematodes.- Reversibility in neural processing systems.- Lyapunov functional
for neural networks with delayed interactions and statistical mechanics of
temporal associations.- Semi-local signal processing in the visual system.-
Statistical mechanics and error-correcting codes.- Synergetic computers An
alternative to neurocomputers.- Dynamics of the Kohonen map.- Equivalence
between connectionist classifiers and logical classifiers.- On Potts-glass
neural networks with biased patterns.- Ising-spin neural networks with
spatial structure.- Kinetically disordered lattice systems.- A programming
system for implementing neural nets.- An auto-augmenting neural network
architecture for diagnostic reasoning.- Formal integrators and neural
networks.- Disorderedmodels of acquired dyslexia.- Higher order memories in
optimally structured neural networks.- Random Boolean networks for
autoassociative memory: Optimization and sequential learning.