Statistical signature of subtle behavioral changes in large-scale assays
Fig 3
(A) Architecture of the self-supervised predictive autoencoder.
The encoder consists of multiple convolutions with ReLU activations alternating between the spatial and temporal axes of the data, followed by a fully connected linear layer. The decoder consists of an upsampling linear layer matching the internal representation to the desired shape, followed by alternating convolutions with ReLU activations. (B) Visualization of the latent space. The 10D latent space is projected into 2D using UMAP [50]. The colors correspond to the discrete behavior dictionary (black: crawl, red: bend, green: stop, blue: hunch, cyan: back, and yellow: roll) (C) Transition probability from one discrete state to another as a function of the position in the latent space: here, between run and bend. (D–F) Highlights of the behavior geometry in the latent space (represented in 2D using UMAP). In D run vs. bend, in E run vs. roll, and in F hunch vs. back. (G) Cross-validated confusion matrix of random forest classifiers using the latent representation to infer the usual discrete behavior dictionary.