End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging
Fig 4
Behavioral state classification from cortical activity using deep learning with RNN.
(A) Schematic overview of the RNN decoder for the behavioral state classification. Input is the cortical activities ranging from 0.5 s before (t−15 frames) to 0.5 s after (t+15 frames) the target frame t, which is labeled with a behavior state (1: run, 0: rest). The RNN decoder outputs the probability of behavioral states for all frames of testing data. (B–D) Example of the GRU decoder performance. (B) Learning curve during training and validation across 30 epochs. Loss indicates the cross entropy loss between the outputs and behavioral labels. Accuracy was the percentage of agreement with the label when the output was binarized at a 0.5 threshold. Mean ± SD, n = 20 models. (C) A trace of the output values of a representative decoder and actual behavioral labels in the first 33.3 s of testing data. (D) The receiver operating characteristic curves in the training, validation, and testing data. (E) The performance of GRU decoders trained with preprocessed data (GRU), non-preprocessed data (Raw), and the decoder of the linear regression model (LR). ***P < 0.001, Wilcoxon rank-sum test with Holm correction, n = 20 models. (F) The decoder performance using six types of RNN architectures. LSTM, GRU, simple RNN (Simple), and their bidirectional ones (Bi-). *P < 0.05, Wilcoxon rank-sum test with Holm correction, n = 20 models.