Authors
Takuya [email protected]
The University of Tokyo, Institute of industrial Science, Tokyo, Japan
Filippo [email protected]
LTI Lab., University of Picardie Jules Verne, Saint-Quentin, France
Takashi [email protected]
The University of Tokyo, Institute of industrial Science, Tokyo, Japan
Available Online 30 June 2018.
DOI
https://doi.org/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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).