RT Journal Article
SR Electronic
T1 Evolving the Olfactory System with Machine Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.15.439917
DO 10.1101/2021.04.15.439917
A1 Peter Y. Wang
A1 Yi Sun
A1 Richard Axel
A1 L.F. Abbott
A1 Guangyu Robert Yang
YR 2021
UL http://biorxiv.org/content/early/2021/04/16/2021.04.15.439917.abstract
AB The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic in vivo would evolve in artificial neural networks constructed to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity to a larger, expansion layer. When trained to both classify odor and impart innate valence on odors, the network develops independent pathways for innate output and odor classification. Thus, artificial networks evolve even without the biological mechanisms necessary to build these systems in vivo, providing a rationale for the convergent evolution of olfactory circuits.Competing Interest StatementThe authors have declared no competing interest.