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This project contains a Matlab implementation of DLAG, a dimensionality reduction framework for disentangling the flow of signals between populations of neurons.

The DLAG model, along with learning and inference procedures, are described in detail in the following reference:

• Gokcen, E., Jasper, A. I., Semedo, J. D., Zandvakili, A., Kohn, A., Machens, C. K. & Yu, B. M. Disentangling the flow of signals between populations of neurons. Nature Computational Science 2, 512–525 (2022). https://doi.org/10.1038/s43588-022-00282-5

Please read it carefully before using the code, as it describes all of the terminology and usage modes. Please cite the above reference if using any portion of this code for your own purposes.

Simply download and extract the latest release of this project to your desired local directory.

You may need to specifically install the Matlab Bioinformatics Toolbox (https://www.mathworks.com/help/bioinfo/index.html) before getting started, if it's not already installed in your Matlab build.

For additional information on getting started, as well as subtler usage details, see this project's wiki here.

For questions, please contact Evren Gokcen at [email protected].

This DLAG implementation started with base code located here.

That project contains implementations of GPFA and TD-GPFA, which are described in the following references:

• Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V. & Sahani, M. Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity. Journal of Neurophysiology 102, 614–635 (2009).

• Lakshmanan, K. C., Sadtler, P. T., Tyler-Kabara, E. C., Batista, A. P. & Yu, B. M. Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays. Neural Computation 27, 1825–1856 (2015).