Open AccessDevelopmental and evolutionary constraints on olfactory circuit selection
Author(s) -
Naoki Hiratani,
Peter E. Latham
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2100600119
Subject(s) - allometry , biology , scaling , layer (electronics) , biological network , selection (genetic algorithm) , computer science , evolutionary biology , artificial neural network , range (aeronautics) , biological system , artificial intelligence , computational biology , ecology , mathematics , nanotechnology , geometry , materials science , composite material
Significance In 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.