Animals estimate visual motion by integrating light intensity information over time and space. The integration requires nonlinear processing, which makes motion estimation circuitry sensitive to specific spatiotemporal correlations that signify visual motion. Classical models of motion estimation weight these correlations to produce direction-selective signals. However, the correlational algorithms they describe have not been directly measured in elementary motion detecting neurons (EMDs). Here, we employed stimuli to directly measure responses to pairwise correlations in Drosophila’s EMD neurons, T4 and T5. Activity in these neurons was required for behavioral responses to pairwise correlations and was predictive of those responses. The pattern of neural responses in the EMDs was inconsistent with one classical model of motion detection, and the timescale and selectivity of correlation responses constrained the temporal filtering properties in potential models. These results reveal how neural responses to pairwise correlations drive visual behavior in this canonical motion detecting circuit.

Highlights d Neurons in Drosophila's early visual system adapt to changes in contrast over time d Strong adaptation can occur quickly, on the timescale of neural responses d Contrast adaptation is heterogeneous across neuron types d Simulations show that contrast adaptation improves naturalistic motion estimation

Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here, we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world’s light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.

No abstract