Presentation 2021-12-18
Optimization and FAGA based implementation of digital spike maps
Tomoki Harada, Toshimichi Saito,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) This paper studies optimization and FPGA based implementation of digital spike maps. First, the dynamics of spike-trains is visualized by a digital spike map. The map is defined on a set of points and is represented by a characteristic vector of integers. Second, we introduce a simple evolutionary algorithm for optimization of digital spike maps. We use autocorrelation function as a cost function. Third, in order to implement the digital spike map, we introduce a digital spiking neuron. Repeating integrate-and-fire behavior between a periodic base signal and constant threshold, the neuron can out put various periodic spike-trains. The digital spike maps are implemented in an FPGA board and typical spike-trains are confirmed experimentally.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Dmap / SEA / FPGA
Paper # NLP2021-58
Date of Issue 2021-12-10 (NLP)

Conference Information
Committee NLP
Conference Date 2021/12/17(2days)
Place (in Japanese) (See Japanese page)
Place (in English) J:COM Horuto Hall OITA
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Takuji Kosaka(Chukyo Univ.)
Vice Chair Akio Tsuneda(Kumamoto Univ.)
Secretary Akio Tsuneda(Kagawa Univ.)
Assistant Hideyuki Kato(Oita Univ.) / Yuichi Yokoi(Nagasaki Univ.)

Paper Information
Registration To Technical Committee on Nonlinear Problems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimization and FAGA based implementation of digital spike maps
Sub Title (in English)
Keyword(1) Dmap
Keyword(2) SEA
Keyword(3) FPGA
1st Author's Name Tomoki Harada
1st Author's Affiliation HOSEI University(HU)
2nd Author's Name Toshimichi Saito
2nd Author's Affiliation HOSEI University(HU)
Date 2021-12-18
Paper # NLP2021-58
Volume (vol) vol.121
Number (no) NLP-307
Page pp.pp.67-71(NLP),
#Pages 5
Date of Issue 2021-12-10 (NLP)