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

Osamu Araki

doi:10.1007/978-3-642-17537-4_29

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

Osamu Araki

Department of Applied Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings
Pages	231-238
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-17537-4_29
Publication status	Published - 2010
Event	17th International Conference on Neural Information Processing, ICONIP 2010 - Sydney, NSW, Australia Duration: 22 Nov 2010 → 25 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6443 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Neural Information Processing, ICONIP 2010
Country/Territory	Australia
City	Sydney, NSW
Period	22/11/10 → 25/11/10

Keywords

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

Access to Document

10.1007/978-3-642-17537-4_29

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

Araki, Osamu. / A stimulus-response neural network model prepared by top-down signals. Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. PART 1. ed. 2010. pp. 231-238 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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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.",

keywords = "neural network model, spike-timing dependent synaptic plasticity, stimulus-response, supplementary motor area, top-down signals",

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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 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6443 LNCS, pp. 231-238, 17th International Conference on Neural Information Processing, ICONIP 2010, Sydney, NSW, Australia, 22/11/10. https://doi.org/10.1007/978-3-642-17537-4_29

A stimulus-response neural network model prepared by top-down signals. / Araki, Osamu.
Neural Information Processing: Theory and Algorithms - 17th International Conference, ICONIP 2010, Proceedings. PART 1. ed. 2010. p. 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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KW - stimulus-response

KW - supplementary motor area

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Araki O. 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. 2010. p. 231-238. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-17537-4_29

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

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