Disentangling the flow of signals between populations of neurons.

Autor: Gokcen E; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA., Jasper AI; Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA., Semedo JD; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA., Zandvakili A; Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA., Kohn A; Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA.; Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, New York, NY, USA.; Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY, USA., Machens CK; Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal., Yu BM; Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. byronyu@cmu.edu.; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. byronyu@cmu.edu.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2022 Aug; Vol. 2 (8), pp. 512-525. Date of Electronic Publication: 2022 Aug 18.
DOI: 10.1038/s43588-022-00282-5
Abstrakt: Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.
(© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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