ArticlePLoS computational biology2024
End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals.Nature methods · 2026Article
- Recent advances in the application of artificial intelligence and wearable devices in volleyball.Frontiers in sports and active living · 2026Review
- Investigation of the heterogeneity of cancer cells using single cell CaCell communication and signaling : CCS · 2025Article
- Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
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4 authors.
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Abstract
Deep learning is a powerful tool for neural decoding, broadly applied to systems neuroscience and clinical studies. Interpretable and transparent models that can explain neural decoding for intended behaviors are crucial to identifying essential features of deep learning decoders in brain activity. In this study, we examine the performance of deep learning to classify mouse behavioral states from mesoscopic cortex-wide calcium imaging data. Our convolutional neural network (CNN)-based end-to-end decoder combined with recurrent neural network (RNN) classifies the behavioral states with high accuracy and robustness to individual differences on temporal scales of sub-seconds. Using the CNN-RNN decoder, we identify that the forelimb and hindlimb areas in the somatosensory cortex significantly contribute to behavioral classification. Our findings imply that the end-to-end approach has the potential to be an interpretable deep learning method with unbiased visualization of critical brain regions.
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