Statistical Mechanics of Neural Networks

Proceedings of the XIth Sitges Conference Sitges, Barcelona, Spain, 3¿7 June 1990
Softcover Nachdruck of the original 1. Auflage 1990
ISBN: 978-3-662-13785-7
Verlag: Springer

Buch, Englisch, Band 368, 477 Seiten, Paperback, Format (B × H): 170 mm x 244 mm, Gewicht: 828 g

Reihe: Lecture Notes in Physics

Combined for researchers and graduate students the articles from the Sitges Summer School together form an excellent survey of the applications of neural-network theory to statistical mechanics and computer-science biophysics. Various mathematical models are presented together with their interpretation, especially those to do with collective behaviour, learning and storage capacity, and dynamical stability.

Zielgruppe

Research

Autoren/Hrsg.

Garrido, Luis

Fachgebiete

• Naturwissenschaften Physik Thermodynamik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik

Inhaltsverzeichnis

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.