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

Takuya Nanami,Filippo Grassia,Takashi Kohno

ICAROB 2018: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS（2018）

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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 ionic-conductance model.

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Key words

Spiking neuron model,Low-threshold spiking,Intrinsically bursting,Differential evolution,FPGA

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