The dynamical analysis of modified two-compartment neuron model and FPGA implementation

Qianjin Lin, Jiang Wang, Shuangming Yang, Guosheng Yi, Bin Deng, Xile Wei and Haitao Yu

Physica A: Statistical Mechanics and its Applications, 2017, vol. 484, issue C, 199-214

Abstract: The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Keywords: Neuronal two-compartment model; Field programmable gate array; Piecewise linearization; Implementation (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:484:y:2017:i:c:p:199-214

DOI: 10.1016/j.physa.2017.04.165

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