具有随机和局部连通性的脉冲神经网络中运动皮层自发放电活动的建模

Lysea Haggie, Thor Besier, Angus JC McMorland
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摘要

大脑皮层活动的计算模型可以让我们深入了解人类大脑的高阶处理机制,包括计划、感知和运动控制。即使在没有感觉输入或可识别的运动的情况下,皮层的活动也在进行,并且被认为与底层皮层电路的拓扑结构有关。然而,连通性及其在产生时空放电模式和皮层计算中的功能作用仍然非常未知。身体的运动是大脑的一个关键功能,运动皮层是涉及运动产生的主要皮层区域。基于Potjans & .的皮层模型,我们建立了一个包含层流结构和电路的运动皮层脉冲神经网络模型。Diesmann(2014)。采用局部连通性方案,增强了大脑皮层模型的生理合理性,并与原始随机连接方法和实验数据比较了局部连通性对神经元放电速率、分布和不规则性的影响。局部连接增加了神经元放电的分布和总体速率。它还导致发射的不规则性与实验测量中观察到的更相似,并且减少了功率谱测量的可变性。局部连接模型动态行为的较大可变性表明,神经元群体中连接的拓扑结构在自发活动期间的放电模式中起着重要作用。该模型旨在采取步骤复制运动皮层的宏观网络,复制真实的放电,以阐明皮层中的信息编码。像这样的大规模计算模型可以捕捉到结构和功能如何与可观察到的神经元放电行为相关联,并研究大脑的潜在计算机制。
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Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity
Computational models of cortical activity can provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernable movements and is thought to be linked to the topology of the underlying cortical circuitry. However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still vastly unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model by Potjans & Diesmann (2014). A local connectivity scheme was implemented to introduce more physiological plausbility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing, was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements, and a reduction in the variability in power spectrum measures. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model aims to take steps towards replicating the macroscopic network of the motor cortex, replicating realistic firing in order to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.
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