Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex.
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex.
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DOI：
10.1016/j.neuron.2016.09.038
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发表时间：
2016-10-19
期刊：
影响因子：
16.2
通讯作者：
Lengyel M
中科院分区：
文献类型：
--
作者：
Orbán G;Berkes P;Fiser J;Lengyel M
视觉皮层的神经反应是可变的，现在有大量的数据来描述这种可变性的大小和结构如何取决于刺激。目前的皮质计算理论无法解释这些数据;他们要么完全忽略了变异性，要么只对其非结构化的泊松性质进行建模。我们开发了一种理论，在该理论中，皮层执行概率推理，使人口活动模式代表从推断的概率分布的统计样本。我们的主要预测是，知觉的不确定性是直接编码的变异性，而不是平均，皮层反应。通过与先前发表的数据以及原始数据分析的直接比较，我们表明，基于采样的概率表示占噪声，信号和自发反应的变异性和相关性在初级视觉皮层的结构。这些结果表明，在皮层动力学和计算的神经变异性的一个新的角色。随机采样将感知不确定性与神经反应变异性联系起来模型解释了反应强度和变异性的独立变化模型预测了噪声、信号和自发相关性之间的关系刺激统计依赖于反应统计的解释Orbán et al.表明，知觉的不确定性与神经元的变异性相联系，解释了初级视觉皮层简单细胞的变异性和协变性的系统变化。该理论还建立了信号，噪声和自发相关性之间的正式关系。
Neural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations. Stochastic sampling links perceptual uncertainty to neural response variability Model accounts for independent changes in strength and variability of responses Model predicts relationship between noise, signal, and spontaneous correlations Stimulus statistics dependence of response statistics is explained Orbán et al. show that linking perceptual uncertainty to neuronal variability accounts for systematic changes in variability and covariability in simple cells of the primary visual cortex. The theory also establishes a formal relationship between signal, noise, and spontaneous correlations.