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Model-based spatiotemporal analysis and control of a network of spiking Basal Ganglia neurons

机译:基于模型的时基基底神经元神经网络的时空分析和控制

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Precise spatiotemporal control of the output of a network of intricately connected neurons through microstimulation is highly desirable in many neural prosthetic applications. This control, however, is challenging, in part due to the large number of unobserved variables in the system under consideration, the complexity underlying the local mechanisms of microstimulation, and the interplay between the intrinsic network structure and its dynamic response to external stimulation. In this work we use a simplified firing rate model, identified from a network of Hodgkin-Huxley (HH) type spiking Basal Ganglia (BG) neurons, to study the response of the network to patterned microstimulation, and to design effective feedback control laws to approximate a desired spatiotemporal pattern. Mathematical analysis of the simplified model using Singular Value Decomposition (SVD) suggests that the BG neural circuit under study exhibits strong spatiotemporal selectivity and only responds strongly to a range of specific spatiotemporal stimulation patterns. We use the concept of functional controllability based on SVD to evaluate the effectiveness of various combinations of stimulation sites for a given set of neurons to be controlled. The results suggest that the functional controllability is largely decided by the network connectivity and the connection strength. Finally, we demonstrate that the controller design based on the simplified model is indeed effective in driving the output neurons to follow a prescribed spatiotemporal firing pattern in the network output.
机译:在许多神经修复应用中,通过微刺激对复杂连接的神经元网络的输出进行精确的时空控制是非常需要的。然而,这种控制具有挑战性,部分原因是所考虑的系统中存在大量未观察到的变量,微刺激的局部机制背后的复杂性以及内在网络结构及其对外部刺激的动态响应之间的相互作用。在这项工作中,我们使用简化的发射率模型(从霍奇金-赫克斯利(HH)型尖峰基底神经节(BG)神经元的网络中识别出)来研究网络对图案化微刺激的响应,并设计有效的反馈控制律以近似所需的时空模式。使用奇异值分解(SVD)对简化模型进行数学分析表明,所研究的BG神经回路表现出较强的时空选择性,并且仅对一系列特定的时空刺激模式有强烈反应。我们使用基于SVD的功能可控性概念来评估给定一组要控制的神经元的刺激部位的各种组合的有效性。结果表明,功能可控性在很大程度上取决于网络连接性和连接强度。最后,我们证明了基于简化模型的控制器设计确实可以有效地驱动输出神经元在网络输出中遵循规定的时空激发模式。

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