Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware.
Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware.
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DOI：
10.3389/fnins.2022.1069133
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发表时间：
2022
影响因子：
4.3
通讯作者：
Kohno, Takashi
中科院分区：
文献类型：
--
作者：
Nanami, Takuya;Kohno, Takashi
尖峰神经元模型模拟神经元的活动，使我们能够分析和再现神经系统的信息处理。然而，离子电导模型，它可以忠实地再现神经元的活动，需要一个巨大的计算成本，而积分放电模型，这是计算成本低，在再现神经元的活动有一些困难。在这里，我们提出了一个分段二次神经元（PQN）模型的基础上的定性建模方法，旨在重现神经元活动背后的关键动力学。我们证明，PQN模型可以准确地再现离子电导模型的主要神经元类的各种幅度的刺激输入的响应。此外，PQN模型的设计，以支持数字运算电路的有效实施，用作硅神经元，我们确认，PQN模型消耗的电路资源少得多的离子电导模型。该模型旨在作为构建大规模更接近生物尖峰神经网络的工具。
Spiking neuron models simulate neuronal activities and allow us to analyze and reproduce the information processing of the nervous system. However, ionic-conductance models, which can faithfully reproduce neuronal activities, require a huge computational cost, while integral-firing models, which are computationally inexpensive, have some difficulties in reproducing neuronal activities. Here we propose a Piecewise Quadratic Neuron (PQN) model based on a qualitative modeling approach that aims to reproduce only the key dynamics behind neuronal activities. We demonstrate that PQN models can accurately reproduce the responses of ionic-conductance models of major neuronal classes to stimulus inputs of various magnitudes. In addition, the PQN model is designed to support the efficient implementation on digital arithmetic circuits for use as silicon neurons, and we confirm that the PQN model consumes much fewer circuit resources than the ionic-conductance models. This model intends to serve as a tool for building a large-scale closer-to-biology spiking neural network.
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影响因子：
3.5
作者：
Ecker A;Romani A;Sáray S;Káli S;Migliore M;Falck J;Lange S;Mercer A;Thomson AM;Muller E;Reimann MW;Ramaswamy S
通讯作者：
Ramaswamy S
影响因子：
2.5
作者：
Butera, RJ;Rinzel, J;Smith, JC
通讯作者：
Smith, JC
DOI：
10.1098/rspb.1984.0024
发表时间：
1984-01-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY SERIES B-BIOLOGICAL SCIENCES
影响因子：
--
作者：
HINDMARSH, JL;ROSE, RM
通讯作者：
ROSE, RM
影响因子：
5.5
作者：
HODGKIN, AL
通讯作者：
HODGKIN, AL
影响因子：
7.8
作者：
Brink, S.;Nease, S.;Hasler, P.
通讯作者：
Hasler, P.