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Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Lappalainen, Janne K. and Tschopp, Fabian D. and Prakhya, Sridhama and McGill, Mason and Nern, Aljoscha and Shinomiya, Kazunori and Takemura, Shin-ya and Gruntman, Eyal and Macke, Jakob H. and Turaga, Srinivas C. (2023) Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution. . (Unpublished)

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We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question. Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected---a universally observed feature of biological neural networks across species and brain regions.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Lappalainen, Janne K.0000-0002-0547-7401
Tschopp, Fabian D.0000-0001-9210-3426
Prakhya, Sridhama0000-0002-1494-0576
McGill, Mason0000-0002-2782-3977
Nern, Aljoscha0000-0002-3822-489X
Shinomiya, Kazunori0000-0003-0262-6421
Takemura, Shin-ya0000-0003-2400-6426
Gruntman, Eyal0000-0003-1383-7347
Macke, Jakob H.0000-0001-5154-8912
Turaga, Srinivas C.0000-0003-3247-6487
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. We are grateful to Lou Scheffer and Lowell Umayam for assistance with accessing connectomic reconstructions. We thank Axel Borst, James Fitzgerald, Nathan Klapoetke, Gerry Rubin, Michael Reiser, and Karel Svoboda for valuable discussions. We thank James Fitzgerald, David Stern, Nathan Klapoetke, Albert Lee, Richard Gao, Jakob Voigts, Brett Mensh for valuable feedback on the manuscript. We thank Tory Herman for sharing the colorization of the optic lobe figure65 (Fig. 1b). This article is subject to HHMI’s Open Access to Publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication. This project was supported by the Howard Hughes Medical Institute. JKL and JHM were supported by the German Research Foundation (DFG) through Germany’s Excellence Strategy (EXC-Number 2064/1, Project number 390727645) and the German Federal Ministry of Education and Research (BMBF; Tübingen AI Center, FKZ: 01IS18039A). JKL is a member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Author Contributions. Conceptualization, Methodology: JKL, FDT, JHM, SCT. Data curation: JKL, FDT, AN, KS, S-yT. Software and Investigation: JKL, MM, SP, FDT. Analysis: JKL, EG, AN, SCT. Writing: JKL, JHM, SCT. Writing (Review & Editing): EG, AN, KS, MM, SP, FDT. Supervision and funding: SCT, JHM. The authors have declared no competing interest.
Funding AgencyGrant Number
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)EXC-2064/1-390727645
Bundesministerium für Bildung und Forschung (BMBF)01IS18039A
International Max Planck Research School for Intelligent SystemsUNSPECIFIED
Record Number:CaltechAUTHORS:20230316-181909000.4
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120123
Deposited By: George Porter
Deposited On:22 Mar 2023 17:00
Last Modified:22 Mar 2023 17:00

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