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Communication Dans Un Congrès Année : 2018

A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model

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Résumé

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.
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Dates et versions

hal-03632177 , version 1 (06-04-2022)

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  • HAL Id : hal-03632177 , version 1

Citer

Takuya Nanami, Filippo Grassia, Takashi Kohno. A Metaheuristic Approach for Parameter Fitting in Digital Spiking Silicon Neuron Model. ICAROB 2018: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS, Feb 2018, Beppu, Japan. pp.465-468. ⟨hal-03632177⟩
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