Authors
Takuya Nanami, Filippo Grassia, Takashi Kohno
Corresponding Author
Takuya Nanami
Available Online 1 June 2017.
DOI
https://doi.org/10.2991/jrnal.2017.4.1.21
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).