Predicting individual neuron responses with anatomically constrained task optimization.

ABSTRACT: Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection

SUBMITTER: Mano O 

PROVIDER: S-EPMC8741219 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature