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Article Dans Une Revue Journal of Robotics, Networking and Artificial Life Année : 2017

A parameter optimization method for Digital Spiking Silicon Neuron model

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Résumé

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.

Dates et versions

hal-03632178 , version 1 (06-04-2022)

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Takuya Nanami, Filippo Grassia, Takashi Kohno. A parameter optimization method for Digital Spiking Silicon Neuron model. Journal of Robotics, Networking and Artificial Life, 2017, 4 (1), pp.97-101. ⟨10.2991/jrnal.2017.4.1.21⟩. ⟨hal-03632178⟩
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