A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model
- 10.2991/jrnal.2018.5.1.8How to use a DOI?
- Spiking neuron model; Low-threshold spiking; Intrinsically bursting; Differential evolution; FPGA
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 © 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 -