| Title: | OS9-4 A parameter optimization method for Digital Spiking Silicon Neuron model |
|---|---|
| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 140-143 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.OS9-4 |
| Author(s): | Takuya Nanami, Takashi Kohno |
| Publication Date: | January 19, 2017 |
| 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 a GPU-based implementation of the differential evolution algorithm in order to reproduce waveforms of the ionic-conductance models and reduce necessary circuit resources for the implementation. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS9/OS9-4.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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