How can we reverse-engineer the brain connectivity, given the input stimulus, and the corresponding brain-activity measurements, for several experiments? We show how to solve the problem in a principled way, modeling the brain as a linear dynamical system (LDS), and solving the resulting "system identification" problem after imposing sparsity and non-negativity constraints on the appropriate matrices. These are reasonable assumptions in some applications, including magnetoencephalography (MEG). There are three contributions: (a) Proof: We prove that this simple condition resolves the ambiguity of similarity transformation in the LDS identification problem; (b) Algorithm, : we propose an effective algorithm which further induces sparse connectivity in a principled way; and (c) Validation: our experiments on semi-synthetic (C. elegans), as well as real MEG data, show that our method recovers the neural connectivity, and it leads to interpretable results.
|Original language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining 2015, SDM 2015|
|Editors||Jieping Ye, Suresh Venkatasubramanian|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2015|
|Event||SIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada|
Duration: Apr 30 2015 → May 2 2015
|Name||SIAM International Conference on Data Mining 2015, SDM 2015|
|Other||SIAM International Conference on Data Mining 2015, SDM 2015|
|Period||4/30/15 → 5/2/15|
Bibliographical noteFunding Information:
Acknowledgments: Research was funded by grants NSF IIS-1247632, NSF IIS-1247489, NSF CDI 0835797, NIH/NICHD 12165321, and a gift from Google. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties.
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