Presentation 1998/7/27
Unsupervised learning of temporal sequence coded by small interval differences
Kenichi Amemori, Shin Ishii,
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Abstract(in English) In this report, we examine unsupervised learning of a sequence of sub-millisecond temporal coded information, in a network of neurons, which are assumed to have high temporal resolution. The input sequence is temporal information that needs the accuracy on the order of submilliseconds.The trained network works like an "associative memory"of the learned sequence, namely, the network responds when a newly input sequence is similar to the learned sequence.Consequently, the assembly of neurons is able to learn and distinguish an input sequence that carries information in the order of sub-milliseconds, although the spike emission intervals of the neurons are on the order of milliseconds.
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Keyword(in English) Temporal coding / Unsupervised learning / Self-organization / Sequential learning / Associative memory
Paper # NC98-42
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Committee NC
Conference Date 1998/7/27(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Unsupervised learning of temporal sequence coded by small interval differences
Sub Title (in English)
Keyword(1) Temporal coding
Keyword(2) Unsupervised learning
Keyword(3) Self-organization
Keyword(4) Sequential learning
Keyword(5) Associative memory
1st Author's Name Kenichi Amemori
1st Author's Affiliation Nara Institute of Science and Technology()
2nd Author's Name Shin Ishii
2nd Author's Affiliation Nara Institute of Science and Technology
Date 1998/7/27
Paper # NC98-42
Volume (vol) vol.98
Number (no) 219
Page pp.pp.-
#Pages 8
Date of Issue