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Neural Circuit Inference from Function to Structure

Real, Esteban and Asari, Hiroki and Gollisch, Tim and Meister, Markus (2017) Neural Circuit Inference from Function to Structure. Current Biology, 27 (2). pp. 189-198. ISSN 0960-9822. https://resolver.caltech.edu/CaltechAUTHORS:20170117-132604622

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Abstract

Advances in technology are opening new windows on the structural connectivity and functional dynamics of brain circuits. Quantitative frameworks are needed that integrate these data from anatomy and physiology. Here, we present a modeling approach that creates such a link. The goal is to infer the structure of a neural circuit from sparse neural recordings, using partial knowledge of its anatomy as a regularizing constraint. We recorded visual responses from the output neurons of the retina, the ganglion cells. We then generated a systematic sequence of circuit models that represents retinal neurons and connections and fitted them to the experimental data. The optimal models faithfully recapitulated the ganglion cell outputs. More importantly, they made predictions about dynamics and connectivity among unobserved neurons internal to the circuit, and these were subsequently confirmed by experiment. This circuit inference framework promises to facilitate the integration and understanding of big data in neuroscience.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.cub.2016.11.040DOIArticle
http://www.sciencedirect.com/science/article/pii/S0960982216313938PublisherArticle
ORCID:
AuthorORCID
Meister, Markus0000-0003-2136-6506
Additional Information:© 2016 Elsevier Ltd. Received 12 September 2016, Revised 17 November 2016, Accepted 17 November 2016, Available online 5 January 2017 Published: January 5, 2017. We would like to thank Ofer Mazor, Haim Sompolinsky, Arjun Krishnaswami, Yoram Burak, Uri Rokni, Andreas Liu, Evan Feinberg, Joel Greenwood, Stan Cotreau, Aravinthan Samuel, and especially Edward Soucy for many useful discussions. This work was supported by Harvard’s Mind/Brain/Behavior Initiative (E.R.), a Gosney postdoctoral fellowship at Caltech (H.A.), and grants from the NIH (7R01EY014737 and 1U01NS090562 to M.M.). Author Contributions: E.R. performed the extracellular array recordings constituting the main dataset. H.A. performed the simultaneous intracellular and extracellular recordings used to test the models. M.M., E.R., and T.G. designed the models; E.R. coded the models and ran the simulations; and E.R. and H.A. analyzed the results. E.R., H.A., and M.M. wrote the manuscript.
Funders:
Funding AgencyGrant Number
Harvard UniversityUNSPECIFIED
CaltechUNSPECIFIED
NIH7R01EY014737
NIH1U01NS090562
Subject Keywords:vision; retina; ganglion cells; bipolar cells; brain circuit; neurophysiology; neural code; computational neuroscience; machine learning; circuit model
Issue or Number:2
Record Number:CaltechAUTHORS:20170117-132604622
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170117-132604622
Official Citation:Esteban Real, Hiroki Asari, Tim Gollisch, Markus Meister, Neural Circuit Inference from Function to Structure, Current Biology, Volume 27, Issue 2, 23 January 2017, Pages 189-198, ISSN 0960-9822, http://dx.doi.org/10.1016/j.cub.2016.11.040. (http://www.sciencedirect.com/science/article/pii/S0960982216313938)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73526
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:18 Jan 2017 00:17
Last Modified:03 Oct 2019 16:28

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