Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012 Paris, France.
Laboratoire de physique statistique, CNRS, Sorbonne Université, Université Paris-Diderot and École normale supérieure (PSL), 24, rue Lhomond, 75005 Paris, France.
eNeuro. 2018 Jan 23;4(6). doi: 10.1523/ENEURO.0166-17.2017. eCollection 2017 Nov-Dec.
Understanding how sensory systems process information depends crucially on identifying which features of the stimulus drive the response of sensory neurons, and which ones leave their response invariant. This task is made difficult by the many nonlinearities that shape sensory processing. Here, we present a novel perturbative approach to understand information processing by sensory neurons, where we linearize their collective response locally in stimulus space. We added small perturbations to reference stimuli and tested if they triggered visible changes in the responses, adapting their amplitude according to the previous responses with closed-loop experiments. We developed a local linear model that accurately predicts the sensitivity of the neural responses to these perturbations. Applying this approach to the rat retina, we estimated the optimal performance of a neural decoder and showed that the nonlinear sensitivity of the retina is consistent with an efficient encoding of stimulus information. Our approach can be used to characterize experimentally the sensitivity of neural systems to external stimuli locally, quantify experimentally the capacity of neural networks to encode sensory information, and relate their activity to behavior.
理解感觉系统如何处理信息，关键在于确定刺激的哪些特征驱动感觉神经元的反应，以及哪些特征使它们的反应保持不变。由于感觉处理中存在许多非线性，因此这项任务变得很困难。在这里，我们提出了一种新的微扰方法来理解感觉神经元的信息处理，在这种方法中，我们在刺激空间中对其集体反应进行局部线性化。我们向参考刺激添加小的扰动，并测试它们是否会引发反应的可见变化，通过闭环实验根据先前的反应来调整它们的幅度。我们开发了一个局部线性模型，该模型可以准确预测神经反应对这些扰动的敏感性。将这种方法应用于大鼠视网膜，我们估计了神经解码器的最佳性能，并表明视网膜的非线性敏感性与刺激信息的有效编码一致。我们的方法可用于局部地实验表征神经系统对外部刺激的敏感性，实验量化神经网络对感觉信息的编码能力，并将其活动与行为联系起来。
