An Introduction to the Modeling of Neural Networks

Authors:
Pierre Peretto
Affiliations:
-
Venue:
An Introduction to the Modeling of Neural Networks
Year:
1992

Citing 0
Cited 11

Probabilistic synaptic transmission in the associative net

Neural Computation
The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network

Neural Processing Letters
The dynamic neural filter: a binary model of spatiotemporal coding

Neural Computation
Associative memory with dynamic synapses

Neural Computation
Neural network based tracking control of a flexible macro-micro manipulator system

Neural Networks
Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks

Neural Computation
Duality between learning machines: A bridge between supervised and unsupervised learning

Neural Computation
Maximum memory capacity on neural networks with short-term synaptic depression and facilitation

Neural Computation
A new stochastic algorithm for strategy optimisation in Bayesian influence diagrams

ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Can dynamic neural filters produce pseudo-random sequences?

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Stochastic techniques in influence diagrams for learning bayesian network structure

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

From the Publisher:This text is a beginning graduate-level introduction to neural networks, focussing on current theoretical models, examining what these models can reveal about how the brain functions, and discussing the ramifications for psychology, artificial intelligence and the construction of a new generation of intelligent computers. The book is divided into four parts. The first part gives an account of the anatomy of the central nervous system, followed by a brief introduction to neurophysiology. The second part is devoted to the dynamics of neuronal states, and demonstrates how very simple models may simulate associative memory. The third part of the book discusses models of learning, including detailed discussions on the limits of memory storage, methods of learning and their associated models, associativity, and error correction. The final part reviews possible applications of neural networks in artificial intelligence, expert systems, optimization problems, and the construction of actual neuronal supercomputers, with the potential for one-hundred-fold increase in speed over contemporary serial machines.