Statistical mechanics of neural networks : proceedings of the XIth Sitges conference, Sitges, Barcelona, Spain, 3-7 June 1990
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 interpretat...
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Edice: | Lecture notes in physics
368 |
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| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Statistical mechanics of neural networks, proceedings of the XIth Sitges conference, Sitges, Barcelona, Spain, 3-7 June 1990, Luis Garrido, (ed.), Berlin, Springer-Verlag, 1990, 1 vol. (VI-477 p.), Lecture notes in physics, 3-540-53267-6 • Statistical Mechanics of Neural Networks, Texte imprimé, 9783662137840 • Statistical Mechanics of Neural Networks, Texte imprimé, 9783662137857 |
Obsah:
- 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.