@article {69399, title = {Optimization in Visual Motion Estimation.}, journal = {Annu Rev Vis Sci}, year = {2024}, month = {2024 Apr 25}, abstract = {

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

}, issn = {2374-4650}, doi = {10.1146/annurev-vision-101623-025432}, author = {Clark, Damon A and Fitzgerald, James E} } @article {65180, title = {Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes.}, journal = {eLife}, volume = {8}, year = {2019}, month = {2019 Oct 15}, abstract = {

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{\textquoteright}s light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

}, issn = {2050-084X}, doi = {10.7554/eLife.47579}, author = {Chen, Juyue and Mandel, Holly B and Fitzgerald, James E and Clark, Damon A} } @article {49528, title = {The neuronal basis of an illusory motion percept is explained by decorrelation of parallel motion pathways.}, journal = {Current Biology : CB}, volume = {28}, year = {2018}, month = {2018 Oct 31}, pages = {3748-78}, abstract = {

Both vertebrates and invertebrates perceive illusory motion, known as "reverse-phi," in visual stimuli that contain sequential luminance increments and decrements. However, increment (ON) and decrement (OFF) signals are initially processed by separate visual neurons, and parallel elementary motion detectors downstream respond selectively to the motion of light or dark edges, often termed ON- and OFF-edges. It remains unknown how and where ON and OFF signals combine to generate reverse-phi motion signals. Here, we show that each of Drosophila{\textquoteright}s elementary motion detectors encodes motion by combining both ON and OFF signals. Their pattern of responses reflects combinations of increments and decrements that co-occur in natural motion, serving to decorrelate their outputs. These results suggest that the general principle of signal decorrelation drives the functional specialization of parallel motion detection channels, including their selectivity for moving light or dark edges.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2018.10.007}, author = {Salazar-Gatzimas, Emilio and Agrochao, Margarida and Fitzgerald, James E and Clark, Damon A} } @article {48387, title = {Nonlinear circuits for naturalistic visual motion estimation.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Oct 24}, pages = {e09123}, abstract = {

Many animals use visual signals to estimate motion. Canonical models suppose that animals estimate motion by cross-correlating pairs of spatiotemporally separated visual signals, but recent experiments indicate that humans and flies perceive motion from higher-order correlations that signify motion in natural environments. Here we show how biologically plausible processing motifs in neural circuits could be tuned to extract this information. We emphasize how known aspects of Drosophila{\textquoteright}s visual circuitry could embody this tuning and predict fly behavior. We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries. Furthermore, a diversity of inputs to motion detecting neurons can provide access to more complex higher-order correlations. Collectively, these results illustrate how non-canonical computations improve motion estimation with naturalistic inputs. This argues that the complexity of the fly{\textquoteright}s motion computations, implemented in its elaborate circuits, represents a valuable feature of its visual motion estimator.

}, keywords = {Animals, Drosophila, Locomotion, Models, Neurological, Motion Perception, Neurons, Visual Pathways}, issn = {2050-084X}, doi = {10.7554/eLife.09123}, author = {Fitzgerald, James E and Clark, Damon A} } @article {48390, title = {Flies and humans share a motion estimation strategy that exploits natural scene statistics.}, journal = {Nature neuroscience}, volume = {17}, year = {2014}, month = {2014 Feb}, pages = {296-303}, abstract = {

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.

}, keywords = {Adaptation, Physiological, Animals, Contrast Sensitivity, Drosophila, Electroencephalography, Evoked Potentials, Visual, Genotype, Humans, Models, Neurological, Motion Perception, Pattern Recognition, Visual, Photic Stimulation, Psychophysics}, issn = {1546-1726}, doi = {10.1038/nn.3600}, author = {Clark, Damon A and Fitzgerald, James E and Ales, Justin M and Gohl, Daryl M and Silies, Marion A and Norcia, Anthony M and Clandinin, Thomas R} }