A stimulus-response neural network model prepared by top-down signals

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Abstract

The stimulus-response circuits in the brain need to have flexible and fast characteristics and these circuits should be activated during the preparatory period for movement. We propose a fundamental neural network model, which can trigger the movement in response to a specific sensory input using top-down signals. When the top-down signal is received in the 1st layer, this circuit waiting for the specific sensory input is in the ready state to move. In response to the specific sensory input, synchrony is emitted and quickly transmitted to the 2nd layer. Because of more convergent connections from 1st to 2nd layers, some synchronous spikes are stably transferred and others are suppressed by the 2nd top-down signal. Thus, appropriate pairing of top-down signals to 1st and 2nd layers enables the circuits to execute an arbitrary stimulus-response behavior.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publicationTheory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
Pages231-238
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 21 Dec 2010
Event17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia
Duration: 22 Nov 201025 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6443 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Neural Information Processing, ICONIP 2010
CountryAustralia
CitySydney, NSW
Period22/11/1025/11/10

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Keywords

  • neural network model
  • spike-timing dependent synaptic plasticity
  • stimulus-response
  • supplementary motor area
  • top-down signals

Cite this

Araki, O. (2010). A stimulus-response neural network model prepared by top-down signals. In Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings (PART 1 ed., pp. 231-238). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6443 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-17537-4_29