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English

Online processing of multiple inputs in a sparsely-connected recurrent neural network

Published inArtificial Neural Networks and Neural Information Processing - ICANN/ICONIP, Editors Kaynak, O.; Alpaydin, E.; Oja, E.; Xu, L., p. 839-845
Presented at Istanbul (Turkey), June 26-29, 2003
PublisherBerlin : Springer
Collection
  • Lecture Notes in Computer Science; 2714/2003
Publication date2003
Abstract

The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is spread throughout the network. We show that simple output structures are powerful enough to extract the diffuse information from the network. The dimensional blow up, which is crucial in kernel methods, is efficiently achieved by the dynamics of the network itself.

Affiliation Not a UNIGE publication
Citation (ISO format)
MAYOR, Julien, GERSTNER, Wulfram. Online processing of multiple inputs in a sparsely-connected recurrent neural network. In: Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP. Istanbul (Turkey). Berlin : Springer, 2003. p. 839–845. (Lecture Notes in Computer Science) doi: 10.1007/3-540-44989-2_100
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ISBN978-3-540-40408-8
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