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首页> 外文期刊>Journal of Computational Neuroscience >Dynamics of temporally interleaved percept-choice sequences: interaction via adaptation in shared neural populations
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Dynamics of temporally interleaved percept-choice sequences: interaction via adaptation in shared neural populations

机译：时间交错感知选择序列的动力学：共享神经群体中通过适应的相互作用。

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At the onset of visually ambiguous or conflicting stimuli, our visual system quickly 'chooses' one of the possible percepts. Interrupted presentation of the same stimuli has revealed that each percept-choice depends strongly on the history of previous choices and the duration of the interruptions. Recent psychophysics and modeling has discovered increasingly rich dynamical structure in such percept-choice sequences, and explained or predicted these patterns in terms of simple neural mechanisms: fast cross-inhibition and slow shunting adaptation that also causes a near-threshold facilitatory effect. However, we still lack a clear understanding of the dynamical interactions between two distinct, temporally interleaved, percept-choice sequences-a type of experiment that probes which feature-level neural network connectivity and dynamics allow the visual system to resolve the vast ambiguity of everyday vision. Here, we fill this gap. We first show that a simple column-structured neural network captures the known phenomenology, and then identify and analyze the crucial underlying mechanism via two stages of model-reduction: A 6-population reduction shows how temporally well-separated sequences become coupled via adaptation in neurons that are shared between the populations driven by either of the two sequences. The essential dynamics can then be reduced further, to a set of iterated adaptation-maps. This enables detailed analysis, resulting in the prediction of phase-diagrams of possible sequence-pair patterns and their response to perturbations. These predictions invite a variety of future experiments.
机译：在出现视觉上模棱两可或相互矛盾的刺激时，我们的视觉系统会迅速“选择”一种可能的感知方式。相同刺激的中断显示表明，每个感知选择都很大程度上取决于先前选择的历史和中断的持续时间。最近的心理物理学和建模已经发现了这种感知选择序列中越来越丰富的动力学结构，并通过简单的神经机制来解释或预测了这些模式：快速交叉抑制和缓慢分流适应也导致了接近阈值的促进作用。但是，我们仍然对两个截然不同的，时间交错的感知选择序列之间的动力学相互作用缺乏清晰的了解。这是一种实验，它探索哪些功能级别的神经网络连通性和动力学特性可以使视觉系统解决日常的巨大歧义视力。在这里，我们填补了这一空白。我们首先显示出一个简单的列结构神经网络捕获了已知的现象学，然后通过模型简化的两个阶段来识别和分析关键的潜在机制：6种群的简化显示了时间上分离良好的序列如何通过适应来耦合由两个序列之一驱动的种群之间共享的神经元。然后可以将基本动态进一步简化为一组迭代的适应图。这可以进行详细分析，从而预测可能的序列对模式的相位图及其对扰动的响应。这些预测邀请了各种未来的实验。

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