davindicode / cosyne-2023-generative-modelsLinks
Notebooks from the workshop tutorial implementing and discussing a range of generative models commonly used in neuroscience.
☆39Updated 2 years ago
Alternatives and similar repositories for cosyne-2023-generative-models
Users that are interested in cosyne-2023-generative-models are comparing it to the libraries listed below
Sorting:
- Code to train and analyze multi-region data-constrained RNNs and perform Current-Based Decomposition (CURBD)☆55Updated 4 years ago
- Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portr…☆91Updated 4 months ago
- Dynamical Similarity Analysis code accompanying the paper "Beyond Geometry: comparing the temporal structure of computation in neural cir…☆68Updated this week
- NEural MOdelS, a statistical modeling framework for neuroscience.☆111Updated this week
- Library to perform Slice Tensor Component Analysis (sliceTCA)☆58Updated 9 months ago
- ☆60Updated 3 years ago
- Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-i…☆85Updated last week
- A repository of neural manifold techniques described and compared in "Neural manifold analysis of brain circuit dynamics in health and di…☆30Updated 2 years ago
- A dimensionality reduction framework for characterizing the multi-dimensional, concurrent flow of signals across multiple neuronal popula…☆15Updated 8 months ago
- Supplementary code for the paper "Linking connectivity, dynamics and computations in low-rank recurrent neural networks" by F. Mastrogius…☆37Updated 7 years ago
- ☆29Updated last month
- Some methods for comparing network representations in deep learning and neuroscience.☆141Updated last year
- Exercises and examples for the latent dynamics workshop☆19Updated 4 months ago
- Sparse Component Analysis: An unsupervised method for recovering interpretable latent factors☆29Updated 9 months ago
- This reading list is centered around the practical application of linear dynamical systems models to predict neural data☆41Updated 4 years ago
- Code accompanying Inferring stochastic low-rank RNNs from neural data. @matthijspals☆25Updated last month
- OpenScope databook: a collaborative, versioned, data-centric collection of foundational analyses for reproducible systems neuroscience 🐁…☆73Updated 4 months ago
- A very simple and barebones tensor decomposition library for CP decomposition a.k.a. PARAFAC a.k.a. TCA☆39Updated 2 years ago
- A dimensionality reduction framework for disentangling the flow of signals between populations of neurons☆32Updated 3 months ago
- ☆82Updated 3 years ago
- Preferential Subspace Identification Algorithm☆54Updated 6 months ago
- State space models for decoding hippocampal trajectories and determining their type using sorted or clusterless data☆52Updated 2 months ago
- ☆31Updated last year
- A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.☆120Updated this week
- A Guide to Reconstructing Dynamical Systems from Neural Measurements Using Recurrent Neural Networks☆41Updated last year
- jPCA for Neural Data Analysis in Python☆52Updated 2 years ago
- Code for Galgali et al, 2023☆14Updated 2 years ago
- Fitting low-rank RNNs to neural trajectories (LINT method).☆18Updated 8 months ago
- ☆56Updated 5 months ago
- ☆55Updated last year