Publication:

The following paper introduces and provides results of DPAD (dissociative and prioritized analysis of dynamics) in multiple real neural datasets.

Omid G. Sani, Bijan Pesaran, Maryam M. Shanechi. Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks. Nature Neuroscience (2024). https://doi.org/10.1038/s41593-024-01731-2

Original preprint: https://doi.org/10.1101/2021.09.03.458628

Usage examples

The following notebooks contains usage examples of DPAD for several use-cases:

1. Simulation notebook: source/DPAD/example/DPAD_tutorial.ipynb.
2. [NEW!] Notebook with real neural-behavioral data: source/DPAD/example/DPAD_tutorial2.ipynb.

Usage examples

The following documents explain the formulation of the key classes that are used to implement DPAD (the code for these key classes is also available in the same directory):

• source/DPAD/DPADModelDoc.md: The formulation implemented by the DPADModel class, which performs the overall 4-step DPAD modeling.

• source/DPAD/RNNModelDoc.md: The formulation implemented by the custom RNNModel class, which implements the RNNs that are trained in steps 1 and 3 of DPAD.

• source/DPAD/RegressionModelDoc.md: The formulation implemented by the RegressionModel class, which RNNModel and DPADModel both internally use to build the general multilayer feed-forward neural networks that are used to implement each model parameter.

We are working on various improvements to the DPAD codebase. Stay tuned!

Change Log

You can see the change log in ChangeLog.md

License

Copyright (c) 2024 University of Southern California
See full notice in LICENSE.md
Omid G. Sani and Maryam M. Shanechi
Shanechi Lab, University of Southern California