Multiple network properties overcome random connectivity to enable stereotypic sensory responses

Aarush Mohit Mittal, Diksha Gupta, Amrita Singh, Andrew C. Lin and Nitin Gupta ()
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Aarush Mohit Mittal: Indian Institute of Technology Kanpur
Diksha Gupta: Indian Institute of Technology Kanpur
Amrita Singh: Indian Institute of Technology Kanpur
Andrew C. Lin: University of Sheffield
Nitin Gupta: Indian Institute of Technology Kanpur

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.

Date: 2020
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DOI: 10.1038/s41467-020-14836-6

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