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选择性视觉注意的单一分层框架中的分裂归一化和神经元振荡。

Divisive normalization and neuronal oscillations in a single hierarchical framework of selective visual attention.

机构信息

Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam Amsterdam, Netherlands.

出版信息

Front Neural Circuits. 2012 May 4;6:22. doi: 10.3389/fncir.2012.00022. eCollection 2012.

Abstract

Divisive normalization models of covert attention commonly use spike rate modulations as indicators of the effect of top-down attention. In addition, an increasing number of studies have shown that top-down attention increases the synchronization of neuronal oscillations as well, particularly in gamma-band frequencies (25-100 Hz). Although modulations of spike rate and synchronous oscillations are not mutually exclusive as mechanisms of attention, there has thus far been little effort to integrate these concepts into a single framework of attention. Here, we aim to provide such a unified framework by expanding the normalization model of attention with a multi-level hierarchical structure and a time dimension; allowing the simulation of a recently reported backward progression of attentional effects along the visual cortical hierarchy. A simple cascade of normalization models simulating different cortical areas is shown to cause signal degradation and a loss of stimulus discriminability over time. To negate this degradation and ensure stable neuronal stimulus representations, we incorporate a kind of oscillatory phase entrainment into our model that has previously been proposed as the "communication-through-coherence" (CTC) hypothesis. Our analysis shows that divisive normalization and oscillation models can complement each other in a unified account of the neural mechanisms of selective visual attention. The resulting hierarchical normalization and oscillation (HNO) model reproduces several additional spatial and temporal aspects of attentional modulation and predicts a latency effect on neuronal responses as a result of cued attention.

摘要

隐蔽注意力的有分歧归一化模型通常使用尖峰率调制作为自上而下注意力影响的指标。此外,越来越多的研究表明,自上而下的注意力也会增加神经元振荡的同步性,特别是在伽马频带频率(25-100 Hz)。虽然尖峰率和同步振荡的调制作为注意力的机制并非互斥,但迄今为止,很少有人努力将这些概念整合到一个单一的注意力框架中。在这里,我们通过扩展带有多层次层次结构和时间维度的注意力归一化模型来提供这样一个统一的框架;允许模拟最近报道的注意力效应沿着视觉皮层层次结构的向后进展。一个简单的归一化模型级联模拟不同的皮层区域,被证明会随着时间的推移导致信号降级和刺激可辨别性的丧失。为了消除这种退化并确保神经元刺激的稳定表示,我们将一种以前被提出作为“通过相干进行通信”(CTC)假设的振荡相位同步纳入我们的模型。我们的分析表明,有分歧的归一化和振荡模型可以在选择性视觉注意力的神经机制的统一解释中相互补充。由此产生的分层归一化和振荡(HNO)模型再现了注意力调制的几个额外的空间和时间方面,并预测了由于提示注意力而导致的神经元反应的潜伏期效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2a1/3343306/5e3a81cbe39e/fncir-06-00022-g001.jpg

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