Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons.

ABSTRACT: Animals have evolved sophisticated visual circuits to solve a vital inference problem: detecting whether or not a visual signal corresponds to an object on a collision course. Such events are detected by specific circuits sensitive to visual looming, or objects increasing in size. Various computational models have been developed for these circuits, but how the collision-detection inference problem itself shapes the computational structures of these circuits remains unknown. Here, inspired by the distinctive structures of LPLC2 neurons in the visual system of Drosophila, we build anatomically-constrained shallow neural network models and train them to identify visual signals that correspond to impending collisions. Surprisingly, the optimization arrives at two distinct, opposing solutions,

SUBMITTER: Zhou B 

PROVIDER: S-EPMC8849349 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature