SPATIAL ORGANIZATION OF NEURAL NETWORKS: A PROBABILISTIC MODELING APPROACH.

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Title

SPATIAL ORGANIZATION OF NEURAL NETWORKS: A PROBABILISTIC MODELING APPROACH. (EN)

Creator

Kontoravdis, Dimitris (EN)

Stafylopatis, Andreas (EN)

Dikaiakos, Marios (EN)

Type

conferenceItem (EN)

Conference item (EN)

Issued

2014-03-01T02:40:51Z

1987 (EN)

Year

1987 (EN)

Description

Summary form only given. A probabilistic model of neural networks was developed using the theory of general product-form queuing networks. A neural network is modeled as an open network of nodes, in which customers moving from node to node represent stimulation and connections between nodes are expressed in terms of suitably selected routing probabilities. A solution of the model is obtained under different disciplines affecting the time spent by a stimulation at each node visited. Results concerning the distribution of excitation as a function of network topology are compared with measures obtained by simulating the behavior of conventional neural networks. (EN)

Subject

NEURAL NETWORKS (EN)

Neural Network (EN)

COMPUTER ARCHITECTURE - Mathematical Models (EN)

PROBABILITY (EN)

Spatial Organization (EN)

PROBABILISTIC MODELING (EN)

SPATIAL ORGANIZATION (EN)

SYSTEMS SCIENCE AND CYBERNETICS (EN)

Probabilistic Model (EN)

Journal

[No source information available] (EN)