A flow-based latent state generative model of neural population responses to natural images
A flow-based latent state generative model of neural population responses to natural images
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基于流的神经群体对自然图像反应的潜在状态生成模型
DOI：
10.1101/2021.09.09.459570
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Fabian H Sinz
中科院分区：
文献类型：
--
作者：
Mohammad Bashiri;Edgar Y. Walker;Konstantin;A. Jagadish;Taliah Muhammad;Zhiwei Ding;Zhuokun Ding;A. Tolias;Fabian H Sinz
我们提出了一个联合深度神经系统识别模型的两个主要来源的神经变异性：刺激驱动和刺激条件波动。为此，我们结合了(1)用于刺激驱动活动的最先进的深度网络和(2)一个灵活的、归一化的基于流的生成模型，以捕获包括噪声相关性在内的刺激条件变异性。这使我们能够端到端训练模型，而不需要与许多刺激条件波动的潜在状态模型相关的复杂概率近似。我们根据小鼠视觉皮层多个区域的数千个神经元对自然图像的反应来训练模型。我们表明，我们的模型在预测神经群体对新刺激的反应分布方面优于先前的最先进的模型，包括共享刺激条件变异性。此外，它成功地学习了已知的群体反应的潜在因素，这些因素与瞳孔扩张等行为变量相关，以及其他随脑面积或视网膜置换位置系统性变化的因素。总的来说，我们的模型准确地解释了神经变异性的两个关键来源，同时避免了与许多现有潜在状态模型相关的几个复杂性。因此，它为揭示导致神经活动变异性的不同因素之间的相互作用提供了一个有用的工具。
We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.
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影响因子：
16.2
作者：
Ecker AS;Berens P;Cotton RJ;Subramaniyan M;Denfield GH;Cadwell CR;Smirnakis SM;Bethge M;Tolias AS
通讯作者：
Tolias AS
影响因子：
25
作者：
Cohen MR;Maunsell JH
通讯作者：
Maunsell JH
影响因子：
4.3
作者：
Cadena, Santiago A.;Denfield, George H.;Ecker, Alexander S.
通讯作者：
Ecker, Alexander S.
影响因子：
25
作者：
Walker, Edgar Y.;Sinz, Fabian H.;Tolias, Andreas S.
通讯作者：
Tolias, Andreas S.