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Memristive biophysical neuron models forming an excitatory–inhibitory neural network for modeling PING rhythm generation
Journal of Computational Electronics ( IF 2.1 ) Pub Date : 2020-09-11 , DOI: 10.1007/s10825-020-01580-9
Melaku Nigus Getachew , Rashmi Priyadarshini , R. M. Mehra

SPICE models are constructed for memristive devices to form associated biophysical neuron circuit models such as the Hodgkin–Huxley (HH) type II excitability neuron circuit model, the HH type III excitability neuron circuit model, the simplified HH neuron circuit model, the Morris–Lecar neuron circuit model, and the memristive based direct-current (DC) circuit model. Rigorous nonlinear circuit-theoretic principles are also applied to analyze the different behaviors of the generic memristor Na\(^{+}\)-ion, K\(^{+}\)-ion, and Ca\(^{++}\)-ion channels forming these biophysical neuron circuit models. Detailed explanations and clarifications are presented on the memristive HH type II and HH type III axonal excitabilities based on mathematical analysis as well as the circuit models. This is done from the perspective of the spike patterns generated by both of these biophysical neuron circuit models. Moreover, various experimental studies have revealed a synchronous brain state known as gamma rhythms that are responsible for sensory, memory, and motor processes. This suggests that understanding how the gamma oscillation (30–100 Hz) is generated in the brain will be extremely important to unravel the link between the activity of an individual neuron and the cognitive processing achieved by a population of networked neurons. We thus also study the dynamics of an interconnected excitatory–inhibitory (E–I) network population, which is ubiquitous in the brain. Utilizing biophysical models of the E–I network, we investigate the generation of pyramidal-interneuronal network gamma (PING) rhythms caused by the external input to the network and the connectivity heterogeneities. The results reveal that synchronous strong PING and sparsely firing weak PING rhythms are generated based on the network connectivities and external input heterogeneities in simulations of 100 memristive HH type II excitability neurons forming an E–I network.



中文翻译:

忆阻性生物物理神经元模型,形成用于模拟PING节奏的兴奋性抑制神经网络

SPICE模型是为忆阻器构建的,以形成相关的生物物理神经元回路模型,例如霍奇金-赫克斯利(HH)II型兴奋性神经元回路模型,HH III型兴奋性神经元回路模型,简化的HH神经元回路模型,Morris-Lecar神经元电路模型和基于忆阻的直流(DC)电路模型。严格的非线性电路理论原理也用于分析通用忆阻器Na \(^ {+} \) -ion,K \(^ {+} \) -ion和Ca \(^ {++ } \)离子通道形成这些生物物理神经元回路模型。基于数学分析和电路模型,对忆阻性HH II型和HH III型轴突兴奋性进行了详细的解释和说明。这是从这两个生物物理神经元电路模型生成的尖峰模式的角度完成的。此外,各种实验研究已经揭示了一种称为伽马节律的同步大脑状态,该状态负责感觉,记忆和运动过程。这表明,了解大脑中伽马振荡的产生方式(30–100 Hz)对于弄清单个神经元的活动与一群网络神经元实现的认知过程之间的联系非常重要。因此,我们还研究了在大脑中普遍存在的相互联系的兴奋性抑制(E–I)网络群体的动力学。利用E–I网络的生物物理模型,我们研究了由网络的外部输入和连通性异质性引起的锥体神经网络间伽玛(PING)节律的产生。结果表明,在模拟形成E–I网络的100个忆阻性HH II型兴奋性神经元的过程中,基于网络连接性和外部输入异质性生成了同步强PING和稀疏激发弱PING节奏。我们调查了由网络的外部输入和连通性异质性引起的金字塔神经网络间伽玛(PING)节律的产生。结果表明,在模拟构成一个E–I网络的100个忆阻性HH II型兴奋性神经元的过程中,基于网络连接性和外部输入异质性生成了同步强PING和稀疏激发弱PING节奏。我们研究了由网络的外部输入和连通性异质性引起的锥体神经网络间伽玛(PING)节律的产生。结果表明,在模拟形成E–I网络的100个忆阻性HH II型兴奋性神经元的过程中,基于网络连接性和外部输入异质性生成了同步强PING和稀疏激发弱PING节奏。

更新日期:2020-09-11
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