arsedler9/lfads-torch
A PyTorch implementation of Latent Factor Analysis via Dynamical Systems (LFADS) and AutoLFADS.
This project helps neuroscientists and researchers clean up and analyze noisy neural spiking activity. You input raw, high-dimensional neural recordings, and it outputs denoised neural firing rates, which are easier to interpret and use for downstream analysis. It is designed for researchers in neuroscience, bioengineering, and related fields working with brain activity data.
127 stars.
Use this if you need to extract clean, underlying neural dynamics from noisy, high-dimensional neural spiking data for scientific or engineering applications.
Not ideal if your data is not neural spiking activity or if you are looking for a simple, out-of-the-box solution without parameter tuning.
Stars
127
Forks
39
Language
Jupyter Notebook
License
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Category
Last pushed
Feb 12, 2026
Commits (30d)
0
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