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• W4220795574 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, only one of which matches the actual dendritic weighting of LPLC2 neurons. Both solutions can solve the inference problem with high accuracy when the population size is large enough. The LPLC2-like solutions reproduces experimentally observed LPLC2 neuron responses for many stimuli, and reproduces canonical tuning of loom sensitive neurons, even though the models are never trained on neural data. Thus, LPLC2 neuron properties and tuning are predicted by optimizing an anatomically-constrained neural network to detect impending collisions. More generally, these results illustrate how optimizing inference tasks that are important for an animal's perceptual goals can reveal and explain computational properties of specific sensory neurons." @default.
• W4220795574 created "2022-04-03" @default.
• W4220795574 creator A5058533213 @default.
• W4220795574 creator A5060219657 @default.
• W4220795574 creator A5060234658 @default.
• W4220795574 creator A5061843649 @default.
• W4220795574 creator A5067424300 @default.
• W4220795574 date "2022-01-13" @default.
• W4220795574 modified "2023-10-03" @default.
• W4220795574 title "Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons" @default.
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• W4220795574 doi "https://doi.org/10.7554/elife.72067" @default.
• W4220795574 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35023828" @default.
• W4220795574 hasPublicationYear "2022" @default.
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