Presentation	2002/1/21 Pulsed neural network models for visual attention Jun SAIKI, Takahiko KOIKE,
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Abstract(in English)	Pulsed neural networks can account for the rapid internal dynamics of visual attention. Integrate-and-fire neuron can function as either a spike counter or a coincidence detector, depending on the time constant of the postsynaptic potential. A model for selective visual attention based on temporal tagging used the temporal property of the neuron models to generate synchronous firing without rate modulation. A model for the saliency map shifts the focus of attention using the internal dynamics of the map alone. Both models could account for the basic findings of visual attention research in neurophysiology and psychology.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	Pulsed neural network / Integrate-and-fire neuron / visual attention / saliency map
Paper #	NC 2001-83
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Conference Information
Committee	NC
Conference Date	2002/1/21(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Pulsed neural network models for visual attention
Sub Title (in English)
Keyword(1)	Pulsed neural network
Keyword(2)	Integrate-and-fire neuron
Keyword(3)	visual attention
Keyword(4)	saliency map
1st Author's Name	Jun SAIKI
1st Author's Affiliation	Graduate School of Informatics, Kyoto University()
2nd Author's Name	Takahiko KOIKE
2nd Author's Affiliation	Graduate School of Informatics, Kyoto University
Date	2002/1/21
Paper #	NC 2001-83
Volume (vol)	vol.101
Number (no)	615
Page	pp.pp.-
#Pages	8
Date of Issue