Evolving the Olfactory System with Machine Learning
Evolving the Olfactory System with Machine Learning
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.
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.