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Disentangling the flow of signals between populations of neurons

Abstract

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.

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Fig. 1: Disentangling the flow of signals between populations of neurons.
Fig. 2: DLAG conceptual illustration.
Fig. 3: Estimates of within- and across-area time courses and their parameters in synthetic data.
Fig. 4: Simultaneous population recordings in V1 and V2.
Fig. 5: Representative DLAG time courses for inter- and intra-areal analyses (same dataset as shown in Fig. 4b).
Fig. 6: Uncovering properties of V1–V2 interactions with DLAG.

Data availability

V1–V2 data are available at the CRCNS data sharing website at https://doi.org/10.6080/K0B27SHN (ref. 70). Naturalistic texture images are available on the Multiband Texture Database at http://multibandtexture.recherche.usherbrooke.ca/original_brodatz.html and on the Salzburg Texture Image Database at https://wavelab.at/sources/STex. Source Data for Figs. 3–6 are available for this Article.

Code availability

A MATLAB implementation of DLAG is available on GitHub at https://github.com/egokcen/DLAG and on Zenodo at https://doi.org/10.5281/zenodo.6654831 (ref. 71).

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Acknowledgements

This work was supported by the Dowd Fellowship (E.G.), Simons Collaboration on the Global Brain 542999 (A.K.), 543009 (C.K.M.), 543065 (B.M.Y.), 364994 (A.K., B.M.Y.), NIH R01 EY028626 (A.K.), NIH U01 NS094288 (C.K.M.), NIH R01 HD071686 (B.M.Y.), NIH CRCNS R01 NS105318 (B.M.Y.), NSF NCS BCS 1533672 and 1734916 (B.M.Y.), NIH CRCNS R01 MH118929 (B.M.Y.) and NIH R01 EB026953 (B.M.Y.).

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E.G., A.I.J., J.D.S., A.K., C.K.M. and B.M.Y. designed the analyses. E.G. derived and implemented DLAG, and performed all analyses. A.I.J., A.Z. and A.K. designed and performed the experiments. E.G., A.I.J., A.K., C.K.M. and B.M.Y. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Byron M. Yu.

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Nature Computational Science thanks Matthew Kaufman, Stephen Keeley, Stefano Recanatesi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Gokcen, E., Jasper, A.I., Semedo, J.D. et al. Disentangling the flow of signals between populations of neurons. Nat Comput Sci 2, 512–525 (2022). https://doi.org/10.1038/s43588-022-00282-5

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