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A class of canonical models for weakly connected neural networks

✍ Scribed by F. Botelho

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
877 KB
Volume
47
Category
Article
ISSN
0362-546X

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✦ Synopsis

In this paper we deduce a class of canonical models for weakly connected neural networks depending on first and second order adaptation conditions. An adaptation condition is a relation involving internal and external network parameters that translates the network's adjustment to environmental stimuli. A qualitative analysis of two dimensional, first order canonical models is presented. Global observations concerning the second order canonical models supporting their potential use as neural simulators is also discussed.

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