Presentation | 2002/12/6 Probabilistic State Transitions in Spiking Neural Network Akiko IWAIZUMI, Ryoko FUTAMI, Nozomu HOSHIMIYA, |
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Abstract(in English) | Male Bengalese finches sing in a fashion that song elements array stochastically each other, which means transition in song elements can be represented as a probabilistic transition matrix. There is no model found that achieve probabilistic state transitions using spiking neuron model. We adopt Hodgkin-Huxley neuron model and propose the model that introduces the network X with auto-associative memory function and mutually inhibited neuron chains with subthreshold noise to make a state of X transit stochastically. We analysed firing rates and temporal structures of each stair of neuron chains to the connection value between X and chains and the number of chains. Numerical simulation revealed that the state transition depends on the firing rate of the last neuron of chain and the connection values determine the firing rate. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Spiking neuron / probabilistic transition matrix / neuron chain / mutual inhibition subthreshold noise |
Paper # | NC2002-97 |
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Committee | NC |
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Conference Date | 2002/12/6(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Probabilistic State Transitions in Spiking Neural Network |
Sub Title (in English) | |
Keyword(1) | Spiking neuron |
Keyword(2) | probabilistic transition matrix |
Keyword(3) | neuron chain |
Keyword(4) | mutual inhibition subthreshold noise |
1st Author's Name | Akiko IWAIZUMI |
1st Author's Affiliation | Department of Electronic Engineering, Tohoku University() |
2nd Author's Name | Ryoko FUTAMI |
2nd Author's Affiliation | Department of Electronic Engineering, Tohoku University |
3rd Author's Name | Nozomu HOSHIMIYA |
3rd Author's Affiliation | Department of Electronic Engineering, Tohoku University |
Date | 2002/12/6 |
Paper # | NC2002-97 |
Volume (vol) | vol.102 |
Number (no) | 508 |
Page | pp.pp.- |
#Pages | 6 |
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