This is the codepack that accompanies "Learning by neural reassociation" by Matthew D. Golub, Patrick T. Sadtler , Emily R. Oby, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, and Byron M. Yu., Nature Neuroscience, 2018. The article can be found online at https://www.nature.com/articles/s41593-018-0095-3.
Thanks to Jay Hennig and Emily Oby for helpful feedback on the codepack.
Codepack version: 1.1
Feedback, comments, suggestions, bug reports, etc, are welcomed and encouraged. Please direct correspondence to Matt Golub (mgolub@stanford.edu).
SETUP:
To run the codepack, you must download CVX for Matlab from www.cvxr.com/cvx/. Place the uncompressed "cvx" folder inside the top-level folder of this codepack.
MATLAB VERSIONS:
This codepack was developed and tested using Matlab R2015a. We have also had success with Matlab R2013a, R2016a, and R2017b. This codepack may not be compatible with earlier Matlab versions (e.g., not compatible with R2011b).
DESCRIPTION:
This codepack includes:
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The optimization routines used to predict after-learning neural activity and behavior according to the 5 hypotheses described in the paper: realignment, rescaling, reassociation, partial realignment, and subselection.
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The primary analysis routines used to compare the experimental data to the predictions of the aforementioned hypotheses. These analyses include repertoire visualization (as in Fig. 3), repertoire change (as in Fig. 4b), covariability along the BCI mappings (as in Fig. 5c), changes in variance vs changes in pushing magnitude (as in Fig. 6f), behavior (as in Fig. 7), and movement-specific repertoire change (as in Fig. 8c).
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Data from a representative experiment (monkey J, 20120305).
Running this script will, for the representative data, generate the predicted neural activity (from 1, above), run the analyses (from 2, above), and generate figures that correspond to these analyses and parallel the paper's main figures.
@ Matt Golub, 2018.