An Efficient and Reconfigurable Synchronous Neuron Model
An Efficient and Reconfigurable Synchronous Neuron Model
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DOI：
10.1109/tcsii.2017.2697826
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
H. Soleimani;E. M. Drakakise
中科院分区：
文献类型：
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作者：
H. Soleimani;E. M. Drakakise
本文提出了一种可重构的、高效的二维神经元模型，能够扩展到更高的维度。该模型应用于Izhikevich和FitzHugh-Nagumo神经元模型作为二维案例研究，并应用于Hindmarsh-Rose模型作为三维案例研究。硬件综合和物理实现表明，与先前发布的分段线性模型相比，所得到的电路可以以可接受的精度和相当低的硬件开销再现神经动力学。
This brief presents a reconfigurable and efficient 2-D neuron model capable of extending to higher dimensions. The model is applied to the Izhikevich and FitzHugh-Nagumo neuron models as 2-D case studies and to the Hindmarsh-Rose model as a 3-D case study. Hardware synthesis and physical implementations show that the resulting circuits can reproduce neural dynamics with acceptable precision and considerably low hardware overhead compared to previously published piecewise linear models.