Dynamical characteristics common to neuronal competition models
Dynamical characteristics common to neuronal competition models
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DOI：
10.1152/jn.00604.2006
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发表时间：
2007-01-01
影响因子：
2.5
通讯作者：
Rubin, Nava
中科院分区：
文献类型：
--
作者：
Shpiro, Asya;Curtu, Rodica;Rubin, Nava
通过相互抑制的种群实现神经元竞争的模型被广泛用于描述双稳态现象，如双眼竞争。我们在这种一般类型的几个模型中发现了共同的动力学行为，这些模型的结构不同，其增益函数的形式不同，而且它们如何实现作为交替支配地位基础的缓慢过程。我们主要考察输入强度对振荡率(以及是否存在)的影响。尽管存在差异，但所有被考虑的模型都具有相似的定性特征，其中一些是我们第一次在这里报道的。在实验上，据报道，优势持续时间随着刺激强度的增加而单调减少(如Levelt的“命题IV”)。这些模型预测了这种行为；然而，他们也预测，在较低的输入强度范围内，主导持续时间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在确定性模型和随机模型中都是常见的。我们的结论是，需要对莱夫特命题IV进行额外的实验测试，以调和模型和认知。
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.