A parameter optimization method for Digital Spiking Silicon Neuron model

DOI

10.2991/jrnal.2017.4.1.21How to use a DOI?

Keywords

Silicon neuronal network, Spiking neuron model, Differential evolution, FPGA

Abstract

DSSN model is a qualitative neuronal model designed for efficient implementation in a digital arithmetic circuit. In our previous studies, we extended this model to support a wide variety of neuronal classes. Parameters of the DSSN model were hand-fitted to reproduce neuronal activity precisely. In this work, we studied automatic parameter fitting procedure for the DSSN model. We optimized parameters of the model by the differential evolution algorithm in order to reproduce waveforms of the ionic-conductance models and reduce necessary circuit resources for the implementation.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Takuya Nanami
AU  - Filippo Grassia
AU  - Takashi Kohno
PY  - 2017
DA  - 2017/06/01
TI  - A parameter optimization method for Digital Spiking Silicon Neuron model
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 97
EP  - 101
VL  - 4
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.1.21
DO  - 10.2991/jrnal.2017.4.1.21
ID  - Nanami2017
ER  -