Connectome-constrained networks predict neural activity across the fly visual system

Janne K. Lappalainen, Fabian D. Tschopp, Sridhama Prakhya, Mason McGill, Aljoscha Nern, Kazunori Shinomiya, Shin-ya Takemura, Eyal Gruntman, Jakob H. Macke and Srinivas C. Turaga ()
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Janne K. Lappalainen: Tübingen University
Fabian D. Tschopp: Howard Hughes Medical Institute
Sridhama Prakhya: Howard Hughes Medical Institute
Mason McGill: Howard Hughes Medical Institute
Aljoscha Nern: Howard Hughes Medical Institute
Kazunori Shinomiya: Howard Hughes Medical Institute
Shin-ya Takemura: Howard Hughes Medical Institute
Eyal Gruntman: Howard Hughes Medical Institute
Jakob H. Macke: Tübingen University
Srinivas C. Turaga: Howard Hughes Medical Institute

Nature, 2024, vol. 634, issue 8036, 1132-1140

Abstract: Abstract We can now measure the connectivity of every neuron in a neural circuit1–9, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question10. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified 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 lobe1–5 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 learning11, to allow the model network to detect visual motion12. 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 26 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.

Date: 2024
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DOI: 10.1038/s41586-024-07939-3

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