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Predicting modular functions and neural coding of behavior from a synaptic wiring diagram

Ashwin Vishwanathan, Alexandro D. Ramirez, Jingpeng Wu, Alex Sood, Runzhe Yang, Nico Kemnitz, Dodam Ih, Nicholas Turner, Kisuk Lee, Ignacio Tartavull, William M. Silversmith, Chris S. Jordan, Celia David, Doug Bland, Mark S. Goldman, Emre R. F. Aksay, H. Sebastian Seung, the Eyewirers
doi: https://doi.org/10.1101/2020.10.28.359620
This article is a preprint and has not been certified by peer review [what does this mean?].
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Abstract

How much can connectomes with synaptic resolution help us understand brain function? An optimistic view is that a connectome is a major determinant of brain function and a key substrate for simulating a brain. Here we investigate the explanatory power of connectomics using a wiring diagram reconstructed from a larval zebrafish brainstem. We identify modules of strongly connected neurons that turn out to be specialized for different behavioral functions, the control of eye and body movements. We then build a neural network model using a synaptic weight matrix based on the reconstructed wiring diagram. This leads to predictions that statistically match the neural coding of eye position as observed by calcium imaging. Our work shows the promise of connectome-based brain modeling to yield experimentally testable predictions of neural activity and behavior, as well as mechanistic explanations of low-dimensional neural dynamics, a widely observed phenomenon in nervous systems.

Competing Interest Statement

HSS has financial interests in Zetta AI LLC.

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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-ND 4.0 International license.
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Posted April 17, 2022.

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