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Dynamical modeling of multi-scale variability in neuronal competition

机译:神经元竞争中多尺度变异的动力学建模

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摘要

Variability is observed at multiple-scales in the brain and ubiquitous in perception. However, the nature of perceptual variability is an open question. We focus on variability during perceptual rivalry, a form of neuronal competition. Rivalry provides a window into neural processing since activity in many brain areas is correlated to the alternating perception rather than a constant ambiguous stimulus. It exhibits robust properties at multiple scales including conscious awareness and neuron dynamics. The prevalent theory for spiking variability is called the balanced state; whereas, the source of perceptual variability is unknown. Here we show that a single biophysical circuit model, satisfying certain mutual inhibition architectures, can explain spiking and perceptual variability during rivalry. These models adhere to a broad set of strict experimental constraints at multiple scales. As we show, the models predict how spiking and perceptual variability changes with stimulus conditions.
机译:在大脑的多个尺度上观察到变异性,并且在感知中无处不在。但是,知觉变异性的性质是一个悬而未决的问题。我们专注于感知竞争中的变异性,这是神经元竞争的一种形式。竞争为神经处理提供了一个窗口,因为许多大脑区域的活动与交替感知相关,而不是持续的模棱两可的刺激。它在包括意识意识和神经元动力学在内的多个尺度上都表现出强大的性能。尖峰变异性的流行理论被称为平衡态。然而,知觉变异性的来源是未知的。在这里,我们表明,满足某些相互抑制架构的单个生物物理电路模型可以解释竞争过程中的尖峰和感知变化。这些模型在多个尺度上都遵循一系列严格的实验约束。正如我们所展示的,这些模型可以预测尖峰和感知变异性如何随刺激条件而变化。

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