Qualitative-Modeling-Based Silicon Neurons and Their Networks.
Qualitative-Modeling-Based Silicon Neurons and Their Networks.
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DOI：
10.3389/fnins.2016.00273
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发表时间：
2016
影响因子：
4.3
通讯作者：
Aihara K
中科院分区：
文献类型：
--
作者：
Kohno T;Sekikawa M;Li J;Nanami T;Aihara K
神经元细胞的离子电导模型可以很好地再现各种复杂的神经元活动。然而，这些模型的复杂性促使定性神经元模型的发展。它们由变量数量减少的微分方程及其低维多项式描述，保留了核心数学结构。这种简单的模型构成了计算和理论神经科学中自下而上方法的基础。提出了一种基于定性建模的硅神经元电路设计方法，其中基于多项式的定性模型中的数学结构由具有硅本征表达式的微分方程再现。这种方法可以实现低功耗电路，可以配置为实现各种类型的神经元细胞。在这篇文章中，我们的定性建模为基础的模拟和数字实现的硅神经元电路快速审查。我们的CMOS模拟硅神经元电路可以实现各种神经元的活动，功耗小于72 nW。并对该电路的方波猝发模式进行了说明。另一个电路可以实现约3nW的I类和II类神经元活动。我们的数字硅神经元电路也可以实现这些类。一个自联想记忆上实现的所有对所有连接的网络，这些硅神经元也审查，其中的神经元类起着重要的作用，在其性能。
The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.