BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
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BehaveNet：行为视频的非线性嵌入和贝叶斯神经解码
DOI：
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
L. Paninski
中科院分区：
文献类型：
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作者：
Eleanor Batty;Matthew R Whiteway;S. Saxena;D. Biderman;Taiga Abe;Simon Musall;Winthrop F. Gillis;J. Markowitz;A. Churchland;J. Cunningham;S. R. Datta;Scott W. Linderman;L. Paninski
系统神经科学的一个基本目标是了解神经活动和行为之间的关系。传统上，行为的特征是低维的，与任务相关的变量，如运动速度或响应时间。最近，人们对实验过程中收集的高维视频数据的自动分析越来越感兴趣。在这里，我们介绍了一个概率框架的行为视频和神经活动的分析。该框架提供了用于压缩、分割、生成和解码行为视频的工具。压缩是使用卷积自动编码器（CAE），它产生一个低维的连续表示的行为。然后，我们使用自回归隐马尔可夫模型（ARHMM）的CAE表示分割成离散的“行为音节。“生成的模型可以用来模拟行为视频数据。最后，基于这种生成模型，我们开发了一种新的贝叶斯解码方法，该方法采用神经活动并输出全分辨率行为视频的概率估计。我们使用不同的行为和神经记录技术在两种不同的实验范式上证明了这个框架。
A fundamental goal of systems neuroscience is to understand the relationship between neural activity and behavior. Behavior has traditionally been characterized by low-dimensional, task-related variables such as movement speed or response times. More recently, there has been a growing interest in automated analysis of high-dimensional video data collected during experiments. Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of behavioral videos. Compression is performed using a convolutional autoencoder (CAE), which yields a low-dimensional continuous representation of behavior. We then use an autoregressive hidden Markov model (ARHMM) to segment the CAE representation into discrete "behavioral syllables." The resulting generative model can be used to simulate behavioral video data. Finally, based on this generative model, we develop a novel Bayesian decoding approach that takes in neural activity and outputs probabilistic estimates of the full-resolution behavioral video. We demonstrate this framework on two different experimental paradigms using distinct behavioral and neural recording technologies.
影响因子：
25
作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
通讯作者：
Bethge, Matthias
用于多尺度建模的树结构循环切换线性动力系统
DOI：
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发表时间：
2019
期刊：
International Conference on Learning Representations (ICLR
影响因子：
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作者：
Nassar, J.;Linderman, S.W.;Bugallo, M.;Park, I.M.
通讯作者：
Park, I.M.
影响因子：
5.7
作者：
Saxena, Shreya;Cunningham, John P.
通讯作者：
Cunningham, John P.