Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)
Disentangled multi-subject and social behavioral representations through a constrained subspace variational autoencoder (CS-VAE)
复制标题
通过约束子空间变分自动编码器（CS-VAE）解开多主体和社会行为表征
DOI：
--
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
S. Saxena
中科院分区：
文献类型：
--
作者：
Daiyao Yi;Simon Musall;A. Churchland;N. Padilla;S. Saxena
有效地建模和量化行为对于我们理解大脑至关重要。在社会和多学科任务中的自然环境中建模行为仍然是一个重大挑战。对执行相同任务的不同主体的行为进行建模需要将行为数据划分为主体之间共有的特征和每个主体不同的其他特征。在一个自由移动的环境中，对多个个体之间的社会互动进行建模，需要与社会调查相比，对个体的影响进行分解。为了将行为灵活地分解为具有个体和跨学科或社会成分的可解释的潜在变量，我们建立了一种半监督方法来划分行为子空间，并提出了一种基于Cauchy-Schwarz散度的新正则化模型。我们的模型，被称为约束子空间变分自动编码器（CS-VAE），成功地模拟了不同主题的行为视频的不同特征，以及社会行为中不断变化的差异。我们的方法极大地促进了对下游任务中所产生的潜在变量的分析，例如发现解开的行为基序，对新主体行为的有效解码，并提供了对相似的不同动物如何执行先天行为的理解。
Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior in naturalistic settings in social and multi-subject tasks remains a significant challenge. Modeling the behavior of different subjects performing the same task requires partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. Modeling social interactions between multiple individuals in a freely-moving setting requires disentangling effects due to the individual as compared to social investigations. To achieve flexible disentanglement of behavior into interpretable latent variables with individual and across-subject or social components, we build on a semi-supervised approach to partition the behavioral subspace, and propose a novel regularization based on the Cauchy-Schwarz divergence to the model. Our model, known as the constrained subspace variational autoencoder (CS-VAE), successfully models distinct features of the behavioral videos across subjects, as well as continuously varying differences in social behavior. Our approach vastly facilitates the analysis of the resulting latent variables in downstream tasks such as uncovering disentangled behavioral motifs, the efficient decoding of a novel subject’s behavior, and provides an understanding of how similarly different animals perform innate behaviors.
影响因子：
25
作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
通讯作者：
Bethge, Matthias