roeegilron / PSID

Preferential Subspace Identification Algorithm

PSID: Preferential subspace identification
[MATLAB implementation]

For Python implementation see http://github.com/ShanechiLab/PyPSID

Given signals y_t (e.g. neural signals) and z_t (e.g behavior), PSID learns a dynamic model for y_t while prioritizing the dynamics that are relevant to z_t.

For the derivation and results in real neural data see the paper below.

Publication:

Omid G. Sani, Hamidreza Abbaspourazad, Yan T. Wong, Bijan Pesaran, Maryam M. Shanechi. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nature Neuroscience, 24, 140–149 (2021). https://doi.org/10.1038/s41593-020-00733-0

View-only full-text link: https://rdcu.be/b993t

Original preprint: https://doi.org/10.1101/808154

You can also find a summary of the paper in the following Twitter thread: https://twitter.com/MaryamShanechi/status/1325835609345122304

Usage guide

Initialization

Add the source directory and its subdirectories to the path. You can run init.m to do this.

Main learning function

The main function for the MATLAB implementation is source/PSID.m. A complete usage guide is available in the function. The following shows an example case:

idSys = PSID(y, z, nx, n1, i);

Inputs:

• y and z are time x dimension matrices with neural (e.g. LFP signal powers or spike counts) and behavioral data (e.g. joint angles, hand position, etc), respectively.
• nx is the total number of latent states to be identified.
• n1 is the number of states that are going to be dedicated to behaviorally relevant dynamics.
• i is the subspace horizon used for modeling.

Output:

• idSys: a structure containing all model parameters (A, Cy, Cz, etc). For a full list see the code.

Extracting latent states using learned model

Once a model is learned using PSID, you can apply the model to new data (i.e. run the associated Kalman filter) as follows:

[zPred, yPred, xPred] = PSIDPredict(idSys, y);

Input:

• y: neural activity time series (time x dimension)

Outputs:

• zPred: one-step ahead prediction of behavior (if any)
• yPred: one-step ahead prediction of neural activity
• xPred: Extracted latent state

Example script

Example simulated data and the script for running PSID on the data is provided in example/example.m This script performs PSID model identification and visualizes the learned eigenvalues similar to in Supplementary Fig 1.

The following notebook also contains some examples with the Python implementation: https://github.com/ShanechiLab/PyPSID/blob/main/source/PSID/example/PSID_tutorial.ipynb

Licence

Copyright (c) 2020 University of Southern California
See full notice in LICENSE.md
Omid G. Sani and Maryam M. Shanechi
Shanechi Lab, University of Southern California