Inception loops discover what excites neurons most using deep predictive models
Inception loops discover what excites neurons most using deep predictive models
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DOI：
10.1038/s41593-019-0517-x
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发表时间：
2019-12-01
影响因子：
25
通讯作者：
Tolias, Andreas S.
中科院分区：
文献类型：
--
作者：
Walker, Edgar Y.;Sinz, Fabian H.;Tolias, Andreas S.
找到最佳驱动神经元的感官刺激是理解大脑信息处理的核心。然而，优化感官输入是困难的，由于主要的非线性性质的感官处理和输入的高维数。我们开发了“初始循环”，这是一种闭环实验范式，将来自数千个神经元的体内记录与计算机非线性响应建模相结合。我们基于深度学习的端到端训练模型预测了数千个神经元对任意新自然输入的反应，具有很高的准确性，并用于合成最佳刺激最令人兴奋的输入（MEI）。对于小鼠初级视觉皮层（V1），MEIs表现出复杂的空间特征，经常发生在自然场景中，但偏离了惊人的共同概念，即Gabor样刺激是最佳的V1。当在体内呈现给相同的神经元时，MEI比对照刺激更好地驱动反应。初始回路是一种广泛应用的解剖感觉神经机制的技术。
Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.