Chalk Matthew, Masset Paul, Deneve Sophie, Gutkin Boris
Institute of Science and Technology Austria, Klosterneuburg, Austria.
Department of Neuroscience, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.
PLoS Comput Biol. 2017 Jun 16;13(6):e1005582. doi: 10.1371/journal.pcbi.1005582. eCollection 2017 Jun.
In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.
为了可靠地响应其环境的特定特征，感觉神经元需要整合多个传入的噪声信号。至关重要的是，它们还需要与代表相似特征的其他神经元竞争对这些信号的解释。这种竞争应采取的形式关键取决于破坏这些信号的噪声。在这项研究中，我们表明，对于感觉系统中常见的噪声类型，其方差随平均信号而缩放，感觉神经元应通过其预测选择性地划分其输入信号，抑制模糊线索同时放大其他线索。刺激背景的任何变化都会改变哪些输入被抑制，从而导致神经感受野的深度动态重塑，远远超出简单的周围抑制。矛盾的是，这些高度可变的感受野与外部感觉特征的不变表示并存，实际上也是其所需。除了提供对感觉反应中上下文依赖变化的规范性解释外，存在信号依赖噪声时的感知推理解释了感觉神经元的普遍特征，如分裂归一化、增益控制和对比度依赖的时间动态。
