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dc.contributor.authorLee, Jeffrey B.
dc.contributor.authorYonar, Abdullah
dc.contributor.authorHallacy, Timothy
dc.contributor.authorShen, Ching-Han
dc.contributor.authorMilloz, Josselin
dc.contributor.authorSrinivasan, Jagan
dc.contributor.authorKocabas, Askin
dc.contributor.authorRamanathan, Sharad
dc.date.accessioned2019-04-12T14:53:10Z
dc.date.issued2018-12-20
dc.identifier.citationLee, Jeffrey B., Abdullah Yonar, Timothy Hallacy, Ching-Han Shen, Josselin Milloz, Jagan Srinivasan, Askin Kocabas, and Sharad Ramanathan. "A Compressed Sensing Framework for Efficient Dissection of Neural Circuits." Nature Methods 16, no. 1 (2019): 126-33.en_US
dc.identifier.issn1548-7091en_US
dc.identifier.issn1548-7105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:39148387*
dc.description.abstractA fundamental question in neuroscience is how neural networks generate behavior. The lack of genetic tools and unique promoters to functionally manipulate specific neuronal subtypes makes it challenging to determine the roles of individual subtypes in behavior. We describe a compressed sensing-based framework in combination with non-specific genetic tools to infer candidate neurons controlling behaviors with fewer measurements than previously thought possible. We tested this framework by inferring interneuron subtypes regulating the speed of locomotion of the nematode Caenorhabditis elegans. We developed a real-time stabilization microscope for accurate long-term, high-magnification imaging and targeted perturbation of neural activity in freely moving animals to validate our inferences. We show that a circuit of three interconnected interneuron subtypes, RMG, AVB and SIA control different aspects of locomotion speed as the animal navigates its environment. Our work suggests that compressed sensing approaches can be used to identify key nodes in complex biological networks.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relationNature Methodsen_US
dc.relation.isversionofhttps://www.nature.com/articles/s41592-018-0233-6en_US
dash.licenseMETA_ONLY
dc.subjectBiotechnologyen_US
dc.subjectCell Biologyen_US
dc.subjectBiochemistryen_US
dc.subjectMolecular Biologyen_US
dc.titleA Compressed Sensing Framework for Efficient Dissection of Neural Circuitsen_US
dc.typeJournal Articleen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalNature Methodsen_US
dash.depositing.authorRamanathan, Sharad
dash.waiver2019-02-08
dc.date.available2019-04-12T14:53:10Z
dash.affiliation.otherHarvard John A. Paulson School of Engineering and Applied Sciencesen_US
dc.identifier.doi10.1038/s41592-018-0233-6
dc.source.journalNat Methods
dash.waiver.reasonRequested by publisheren_US
dash.source.volume16;1
dash.source.page126-133
dash.contributor.affiliatedYonar, Abdullah
dash.contributor.affiliatedHallacy, Timothy
dash.contributor.affiliatedSrinivasan, Jagan
dash.contributor.affiliatedKocabas, Askin
dash.contributor.affiliatedRamanathan, Sharad


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