Journal
NATURE NEUROSCIENCE
Volume 24, Issue 1, Pages 140-149Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41593-020-00733-0
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Funding
- Army Research Office (ARO) [W911NF-16-1-0368]
- US DOD
- UK Engineering and Physical Research Council (EPSRC) under the Multidisciplinary University Research Initiative (MURI)
- Office of Naval Research (ONR) Young Investigator Program (YIP) [N00014-19-1-2128]
- National Science Foundation (NSF) [CCF-1453868]
- ARO [W911NF1810434]
- Bilateral Academic Research Initiative (BARI)
- US National Institutes of Health (NIH) BRAIN [R01-NS104923]
- University of Southern California Annenberg Fellowship
- U.S. Department of Defense (DOD) [W911NF1810434] Funding Source: U.S. Department of Defense (DOD)
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The PSID algorithm is developed to model neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Through simulation, it was found that behaviorally relevant dynamics are lower-dimensional than previously expected, and can be more accurately learned using PSID.
Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Modeling data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more accurately learned behaviorally relevant dynamics for each joint and recording channel. Finally, modeling data in two monkeys performing saccades demonstrated the generalization of PSID across behaviors, brain regions and neural signal types. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed. This work develops PSID, a dynamic modeling method to dissociate and prioritize neural dynamics relevant to a given behavior.
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