NeuralFlow
Short description
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:
- 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
- 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
Installation and documentation
https://neuralflow.readthedocs.io/
Tutorial
Part 1: Data format
Convert data from the spike times format to the ISI format.
Part 2: EnergyModel Class
Create EnergyModel class and visualize the framework parameters.
Part 3: Synthetic data generation
Generate synthetic data and latent trajectories from the ramping dynamics and visualize the latent trajectories, firing rate along these trajectories, and the spike rasters.
Part 4: Model Inference
Optimize a model potential on spike data generated from the ramping dynamics.
Part 5: Feature consistency analysis for model selection
Implement feature consistency analysis for model selection.