Statistical mechanics of neural networks : proceedings of the XIth Sitges conference, Sitges, Barcelona, Spain, 3-7 June 1990 / Luis Garrido, (ed.).

Contributor(s): Garrido, L. (Luis), 1930-
Material type: TextTextSeries: Lecture notes in physics: 368.Publisher: Berlin ; New York : Springer-Verlag, ©1990Description: 1 online resource (vi, 477 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540468080; 3540468080Subject(s): Neural networks (Neurobiology) -- Mathematical models -- Congresses | Neural networks (Computer science) -- Congresses | Statistical mechanics -- Congresses | Neural networks (Computer science) | Neural networks (Neurobiology) -- Mathematical models | Statistical mechanics | Neurale netwerken | Statistische mechanica | Réseaux neuronaux (informatique) -- Congrès | Réseaux neuronaux (physiologie) -- Congrès | Mécanique statistique -- Congrès | Neuronales Netz | Statistische Mechanik | Barcelona <1990>Genre/Form: Electronic books. | Conference papers and proceedings. Additional physical formats: Print version:: Statistical mechanics of neural networks.DDC classification: 006.3 LOC classification: QP363.3 | .S57 1990Other classification: 54.72 Online resources: Click here to access online
Contents:
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 -- Disordered models of acquired dyslexia -- Higher order memories in optimally structured neural networks -- Random Boolean networks for autoassociative memory: Optimization and sequential learning.
Action note: digitized 2010 committed to preserveSummary: 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.
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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.

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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 -- Disordered models of acquired dyslexia -- Higher order memories in optimally structured neural networks -- Random Boolean networks for autoassociative memory: Optimization and sequential learning.

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