Presentation 1996/6/21
Generation of Random Patterns of Neuronal Activity by Network Models and its Mathematical Properties
Akira Date,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Modified Hebbian learning rule (Griniasty et al. 1993) or trace mechanism (Foldiak 1992)is one account for the problem of how the temporal relations among events are represented in neural connections. Here we show another possibility to consider random encoding strategy in which items to be stored are associated with the randomly chosen attractors of a network independent of spatial relationships between them. Namely the network spontaneously generate random patterns of activity as internal representations for events or objects. This was demonstrated by the network models in which connection weights are i.i.d.random variables under the symmetric condition. Mathematical properties on this system, such as distances between randomly chosen attractors, methods generating the random patterns and the dependencies of the methods, are theoretically considered. Lastly, we discuss its relevances to the physiological evidences for memory.
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Keyword(in English) memory / attractor / random encoding / random symmetric neural network
Paper # NC96-14
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Committee NC
Conference Date 1996/6/21(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Generation of Random Patterns of Neuronal Activity by Network Models and its Mathematical Properties
Sub Title (in English)
Keyword(1) memory
Keyword(2) attractor
Keyword(3) random encoding
Keyword(4) random symmetric neural network
1st Author's Name Akira Date
1st Author's Affiliation Division of Electronic and Information Engineering, Graduate School of Technology, Tokyo University of Agriculture & Technology()
Date 1996/6/21
Paper # NC96-14
Volume (vol) vol.96
Number (no) 117
Page pp.pp.-
#Pages 8
Date of Issue