| Title: | OS9-2 A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model |
|---|---|
| Publication: | ICAROB2018 |
| Volume: | 23 |
| Pages: | 465-468 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2018.OS9-2 |
| Author(s): | Takuya Nanami, Filippo Grassia, Takashi Kohno |
| Publication Date: | February 2, 2018 |
| 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 ionicconductance model. |
| PDF File: | https://alife-robotics.co.jp/members2018/icarob/data/html/data/OS_pdf/OS9/OS9-2.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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