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.2018.5.1.8
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/).