Global motion processing by populations of direction-selective retinal ganglion cells

Cafaro, Jon; Zylberberg, Joel; Field, Greg D.

doi:10.1101/572438

Cited by 5 publications

(5 citation statements)

References 75 publications

(108 reference statements)

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“…The solution depends on the number of detectors, and this is likely related to the increasing overlap in receptive fields as the population grows (Figure 6). This result is consistent with prior work showing that populations of neurons often exhibit different and improved coding strategies compared to individual neurons [Pasupathy and Connor, 2002, Georgopoulos et al, 1986, Vogels, 1990, Franke et al, 2016, Zylberberg et al, 2016, Cafaro et al, 2020. Thus, understanding anatomical, physiological, and algorithmic properties of individual neurons can require considering the population response.…”

Section: Discussionsupporting

confidence: 89%

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

Zhou

Ssy

et al. 2021

Preprint

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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 an anatomically-constrained shallow neural network model and train it 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. The LPLC2-like solutions are favored when a population of units is trained on the task, but not when units are trained in isolation. The trained model reproduces experimentally observed LPLC2 neuron responses for many stimuli, and reproduces canonical tuning of loom sensitive neurons, even though the model are never trained on neural data. These results show that LPLC2 neuron properties and tuning are predicted by optimizing an anatomically-constrained neural network to detect impending collisions.

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Section: Discussionsupporting

confidence: 89%

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

Zhou

Ssy

et al. 2021

Preprint

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“…The encoding of optic flow generated by self-motion at the level of local motion detectors has also recently been described in the mouse retina (1), where any kind of self-motion will activate different retinal ganglion cell types from both eyes in a unique pattern that will be decomposed into translational and rotational components further downstream. The fly eye and the vertebrate retina both show differences between local and global directional tuning (1,49), and similarly compute visual signals generated by selfmotion at the population level (1). A population code for optic flow generated by self-motion might therefore be a canonical strategy of visual systems and evolved convergently during evolution.…”

Section: Discussionmentioning

confidence: 99%

Populations of local direction–selective cells encode global motion patterns generated by self-motion

et al. 2022

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Self-motion generates visual patterns on the eye that are important for navigation. These optic flow patterns are encoded by the population of local direction–selective cells in the mouse retina, whereas in flies, local direction–selective T4/T5 cells are thought to be uniformly tuned. How complex global motion patterns can be computed downstream is unclear. We show that the population of T4/T5 cells in Drosophila encodes global motion patterns. Whereas the mouse retina encodes four types of optic flow, the fly visual system encodes six. This matches the larger number of degrees of freedom and the increased complexity of translational and rotational motion patterns during flight. The four uniformly tuned T4/T5 subtypes described previously represent a local subset of the population. Thus, a population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal’s movement.

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“…The fly eye and the vertebrate retina both show differences between local and global directional tuning(7, 42), and similarly compute visual signals generated by self-motion at the population level(7). A population code for optic flow generated by self-motion might therefore be canonical and evolved convergently during evolution.…”

Section: Discussionmentioning

confidence: 99%

An optimal population code for global motion estimation in local direction-selective cells

Henning

Ramos-Traslosheros

Gür

et al. 2021

Preprint

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Nervous systems allocate computational resources to match stimulus statistics. However, the physical information that needs to be processed depends on the animal's own behavior. For example, visual motion patterns induced by self-motion provide essential information for navigation. How behavioral constraints affect neural processing is not known. Here we show that, at the population level, local direction-selective T4/T5 neurons in Drosophila represent optic flow fields generated by self-motion, reminiscent to a population code in retinal ganglion cells in vertebrates. Whereas in vertebrates four different cell types encode different optic flow fields, the four uniformly tuned T4/T5 subtypes described previously represent a local snapshot. As a population, six T4/T5 subtypes encode different axes of self-motion. This representation might serve to efficiently encode more complex flow fields generated during flight. Thus, a population code for optic flow appears to be a general coding principle of visual systems, but matching the animal's individual ethological constraints.

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Global motion processing by populations of direction-selective retinal ganglion cells

Cited by 5 publications

References 75 publications

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

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

Populations of local direction–selective cells encode global motion patterns generated by self-motion

An optimal population code for global motion estimation in local direction-selective cells

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