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Found 4 papers, 3 papers with code
Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity
Overall, ODIN's accuracy in recovering ground-truth latent features and ability to accurately reconstruct neural activity with low dimensionality make it a promising method for distilling interpretable dynamics that can help explain neural computation.
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional neural activity for downstream applications in science and engineering.
Expressive architectures enhance interpretability of dynamics-based neural population models
Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underlying biological computation.
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data.
