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Developmental and evolutionary constraints on olfactory circuit selection.

ABSTRACT: SignificanceIn this work, we explore the hypothesis that biological neural networks optimize their architecture, through evolution, for learning. We study early olfactory circuits of mammals and insects, which have relatively similar structure but a huge diversity in size. We approximate these circuits as three-layer networks and estimate, analytically, the scaling of the optimal hidden-layer size with input-layer size. We find that both longevity and information in the genome constrain the hidden-layer size, so a range of allometric scalings is possible. However, the experimentally observed allometric scalings in mammals and insects are consistent with biologically plausible values. This analysis should pave the way for a deeper understanding of both biological and artificial networks.

SUBMITTER: Hiratani N

PROVIDER: S-EPMC8931209 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Developmental and evolutionary constraints on olfactory circuit selection.

Hiratani Naoki N Latham Peter E PE

Proceedings of the National Academy of Sciences of the United States of America 20220309 11

SignificanceIn this work, we explore the hypothesis that biological neural networks optimize their architecture, through evolution, for learning. We study early olfactory circuits of mammals and insects, which have relatively similar structure but a huge diversity in size. We approximate these circuits as three-layer networks and estimate, analytically, the scaling of the optimal hidden-layer size with input-layer size. We find that both longevity and information in the genome constrain the hidd ...[more]

PMID: 35263217

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