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Dynamical Characteristics Common to Neuronal Competition Models

机译:神经元竞争模型共有的动力学特征

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摘要

Models implementing neuronal competition by reciprocally inhibitory populations are widely used to characterize bistable phenomena such as binocular rivalry. We find common dynamical behavior in several models of this general type, which differ in their architecture in the form of their gain functions, and in how they implement the slow process that underlies alternating dominance. We focus on examining the effect of the input strength on the rate (and existence) of oscillations. In spite of their differences, all considered models possess similar qualitative features, some of which we report here for the first time. Experimentally, dominance durations have been reported to decrease monotonically with increasing stimulus strength (such as Levelt's “Proposition IV”). The models predict this behavior; however, they also predict that at a lower range of input strength dominance durations increase with increasing stimulus strength. The nonmonotonic dependency of duration on stimulus strength is common to both deterministic and stochastic models. We conclude that additional experimental tests of Levelt's Proposition IV are needed to reconcile models and perception.
机译:通过相互抑制种群实现神经元竞争的模型被广泛用于表征双稳态现象,例如双眼竞争。我们在几种这种通用类型的模型中发现了共同的动力学行为,这些动力学模型的结构不同,其增益函数的形式不同,它们如何实现构成交替优势的缓慢过程。我们专注于检查输入强度对振荡速率(和存在)的影响。尽管它们之间存在差异,但所有考虑的模型都具有相似的定性特征,其中一些是我们首次在此报告。实验上,据报道,随着刺激强度的增加,主导权的持续时间会单调减少(例如Levelt的“提议IV”)。这些模型可以预测这种行为。然而,他们还预测,在较低的输入强度范围内,持续时间会随着刺激强度的增加而增加。持续时间对刺激强度的非单调依赖性在确定性模型和随机模型中都是常见的。我们得出结论,需要对Levelt命题IV进行额外的实验测试,以使模型和感知协调一致。

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