vruetten / GPFADSLinks
Gaussian Process Factor Analysis with Dynamical Structure
☆16Updated 4 years ago
Alternatives and similar repositories for GPFADS
Users that are interested in GPFADS are comparing it to the libraries listed below
Sorting:
- Code for Galgali et al, 2023☆13Updated 2 years ago
- ☆23Updated last year
- Exercises and examples for the latent dynamics workshop☆17Updated last year
- This code package is for the Tensor-Maximum-Entropy (TME) method. This method generates random surrogate data that preserves a specified …☆18Updated 7 years ago
- Supplementary code for the paper "Linking connectivity, dynamics and computations in low-rank recurrent neural networks" by F. Mastrogius…☆34Updated 6 years ago
- Pytorch implementation of lfads, and hierarchical extension☆26Updated 3 years ago
- A TensorFlow 2.0 implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.☆21Updated last year
- Code for "Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations"☆10Updated 3 years ago
- Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend☆59Updated 11 months ago
- Notebooks from the workshop tutorial implementing and discussing a range of generative models commonly used in neuroscience.☆38Updated 2 years ago
- Dimensionality reduction of spikes trains☆49Updated 3 years ago
- ☆20Updated last year
- latent manifold tuning model / P-GPLVM☆12Updated 5 years ago
- Dynamical Components Analysis☆32Updated last year
- ☆20Updated 3 months ago
- A dimensionality reduction framework for characterizing the multi-dimensional, concurrent flow of signals across multiple neuronal popula…☆16Updated last month
- Recurrent Switching Linear Dynamical Systems☆111Updated 2 years ago
- ☆13Updated 2 years ago
- Demixed Shared Component Analysis☆13Updated 4 years ago
- A very simple and barebones tensor decomposition library for CP decomposition a.k.a. PARAFAC a.k.a. TCA☆35Updated last year
- Code accompanying Inferring stochastic low-rank RNNs from neural data. @matthijspals☆20Updated last month
- Fitting low-rank RNNs to neural trajectories (LINT method).☆16Updated 2 months ago
- Neyman-Scott point process model to identify sequential firing patterns in high-dimensional spike trains☆66Updated last year
- Pytorch implementation of LFADS for demo at CAN workshop☆19Updated 5 years ago
- Recurrent state-space models for decision making☆30Updated 2 years ago
- NeuroTask: A Benchmark Dataset for Multi-Task Neural Analysis☆12Updated last month
- ☆52Updated 2 years ago
- Simulations illustrating the results of full-FORCE.☆13Updated 6 years ago
- Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics☆10Updated last year
- Poisson Identifiable VAE (pi-VAE)☆51Updated 3 years ago