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• W2970371569 abstract "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 video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of 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 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 video. We demonstrate this framework on two different experimental paradigms using distinct and neural recording technologies." @default.
• W2970371569 created "2019-09-05" @default.
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• W2970371569 date "2019-09-06" @default.
• W2970371569 modified "2023-09-24" @default.
• W2970371569 title "BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos" @default.
• W2970371569 hasPublicationYear "2019" @default.
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