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Pattern separability and the effect of the number of connections in a random neural net with inhibitory connections

โœ Scribed by Toyoshi Torioka

Publisher
Springer-Verlag
Year
1978
Tongue
English
Weight
630 KB
Volume
31
Category
Article
ISSN
0340-1200

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โœฆ Synopsis

It has been claimed that pattern separation in cerebellar cortex plays an important role in controlling movements and balance for vertebrates. A number of the neural models for cerebellar cortex have been proposed and their pattern separability has been analyzed. These results, however, only explain a part of pattern separability in random neural nets. The present paper is intended to study an extended theory of pattern separability in a new model with inhibitory connections. In addition to this, the effect of the number of connections on pattern separability is cleared up. It is also shown that the signal from the inhibitory connections has crucial importance for pattern separability.

๐Ÿ“œ SIMILAR VOLUMES

Pattern separability in a random neural
Pattern separability in a random neural net with inhibitory connections
โœ T. Torioka ๐Ÿ“‚ Article ๐Ÿ“… 1979 ๐Ÿ› Springer-Verlag ๐ŸŒ English โš– 673 KB

Some interesting properties on pattern separation have been shown through researches by neural models of cerebellar cortex. It seems to us that those results are a part of the properties of pattern separation. A two layer random nerve net with inhibitory connections is given as a model of the cerebe