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Found 5 papers, 3 papers with code
Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes
Here we propose a new approach to neural data analysis that leverages advances in conditional generative modeling to enable the unsupervised inference of disentangled behavioral variables from recorded neural activity.
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.
