https://github.com/engellab/neuralflow

The framework for inferring Langevin dynamics from spike data
https://github.com/engellab/neuralflow

Last synced: 4 days ago
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The framework for inferring Langevin dynamics from spike data

• Host: GitHub
• URL: https://github.com/engellab/neuralflow
• Owner: engellab
• License: mit
• Created: 2020-08-24T16:48:54.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2023-08-23T05:00:27.000Z (10 months ago)
• Last Synced: 2024-03-02T13:33:18.194Z (4 months ago)
• Language: Jupyter Notebook
• Size: 26.7 MB
• Stars: 25
• Watchers: 4
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-stars - neuralflow

README

        
#  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:

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

## Installation and documentation

https://neuralflow.readthedocs.io/

## Tutorial

### Part 1: Data format

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

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/engellab/neuralflow/blob/master/tutorials/CCN2021/Exercises/Ex1_Data_Format.ipynb)

### Part 2: EnergyModel Class

Create EnergyModel class and visualize the framework parameters.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/engellab/neuralflow/blob/master/tutorials/CCN2021/Exercises/Ex2_EnergyModel_class.ipynb)

### 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.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/engellab/neuralflow/blob/master/tutorials/CCN2021/Exercises/Ex3_Data_Generation.ipynb)

### Part 4: Model Inference

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

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/engellab/neuralflow/blob/master/tutorials/CCN2021/Exercises/Ex4_Model_Optimization.ipynb)

### Part 5: Feature consistency analysis for model selection

Implement feature consistency analysis for model selection.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/engellab/neuralflow/blob/master/tutorials/CCN2021/Exercises/Ex5_Feature_Consistency_Analysis.ipynb)