Computational framework for modeling neural activity with continuous latent Langevin dynamics.

Quick installation: pip install git+https://github.com/engellab/neuralflow

The source code for the following publications:

1. Genkin, M., Hughes, O. and Engel, T.A., 2020. Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories. Nat Commun 12, 5986 (2021).

Link: https://rdcu.be/czqGP

2. Genkin, M., Engel, T.A. Moving beyond generalization to accurate interpretation of flexible models. Nat Mach Intell 2, 674–683 (2020).

Link: https://www.nature.com/articles/s42256-020-00242-6/

Free access: https://rdcu.be/b9cW3

https://neuralflow.readthedocs.io/

Convert data from the spike times format to the ISI format.

Create EnergyModel class and visualize the framework parameters.

Generate synthetic data and latent trajectories from the ramping dynamics and visualize the latent trajectories, firing rate along these trajectories, and the spike rasters.

Optimize a model potential on spike data generated from the ramping dynamics.

Implement feature consistency analysis for model selection.