Journal of Robotics, Networking and Artificial Life

Volume 5, Issue 1, June 2018, Pages 32 - 36

A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model

Authors
Takuya Nanaminanami@sat.t.u-tokyo.ac.jp
The University of Tokyo, Institute of industrial Science, Tokyo, Japan
Filippo Grassiafilippo.grassia@u-picardie.fr
LTI Lab., University of Picardie Jules Verne, Saint-Quentin, France
Takashi Kohnokohno@sat.t.u-tokyo.ac.jp
The University of Tokyo, Institute of industrial Science, Tokyo, Japan
Available Online 30 June 2018.
DOI
10.2991/jrnal.2018.5.1.8How to use a DOI?
Keywords
Spiking neuron model; Low-threshold spiking; Intrinsically bursting; Differential evolution; FPGA
Abstract

DSSN model is a qualitative neuronal model designed for efficient implementation in digital arithmetic circuit. In our previous studies, we developed automatic parameter fitting method using the differential evolution algorithm for regular and fast spiking neuron classes. In this work, we extended the method to cover low-threshold spiking and intrinsically bursting. We optimized parameters of the DSSN model in order to reproduce the reference ionic-conductance model.

Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
5 - 1
Pages
32 - 36
Publication Date
2018/06/30
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2018.5.1.8How to use a DOI?
Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Takuya Nanami
AU  - Filippo Grassia
AU  - Takashi Kohno
PY  - 2018
DA  - 2018/06/30
TI  - A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 32
EP  - 36
VL  - 5
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.1.8
DO  - 10.2991/jrnal.2018.5.1.8
ID  - Nanami2018
ER  -