Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.
Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.
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用于数字算术电路实现的简单皮质和丘脑神经元模型。
DOI：
10.3389/fnins.2016.00181
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发表时间：
2016
影响因子：
4.3
通讯作者：
Kohno T
中科院分区：
文献类型：
--
作者：
Nanami T;Kohno T
神经元活动的再现性与计算效率之间的权衡是计算神经科学和神经形态工程的重要课题之一。从不同的角度研究了各种各样的神经元模型。数字脉冲硅神经元（dsn）模型是一种专注于通过数字运算电路高效实现的定性模型。我们扩展了dsn模型，并找到了合适的参数集，它可以再现四类皮质和丘脑神经元的离子电导模型的动态行为。我们首先通过减少离子电导模型中变量的数量，建立了一个四变量模型，并利用分岔分析阐明了其数学结构。然后，构建扩展的dsn模型来再现这些数学结构并捕获每个神经元类的特征行为。我们证实，在dsn和离子电导模型中，神经元尖峰序列的统计数据是相似的。dsn模型的计算成本比目前复杂的基于集成和发射的模型要大，但比离子电导模型要小。该模型旨在为上述权衡提供另一个交汇点，以满足更接近生物学模型的大规模神经网络模拟的需求。
Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models.