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Arvin, S.

Publications and source records attributed to Arvin, S..

2 recordsLinked to original sources

Binocular integration of retinal motion information underlies optic flow processing by the cortex

Locomotion creates various patterns of optic flow on the retina, which provide the observer with information about their movement relative to the environment. However, it is unclear how these optic flow patterns are encoded by the cortex. Here we use two-photon calcium imaging in awake mice to systematically map monocular and binocular responses to horizontal motion in four areas of the visual cortex. We find that neurons selective to translational or rotational optic flow are abundant in higher visual areas, whereas neurons suppressed by binocular motion are more common in the primary visual cortex. Disruption of retinal direction selectivity in Frmd7 mutant mice reduces the number of translation-selective neurons in the primary visual cortex, and translation- and rotation-selective neurons as well as binocular direction-selective neurons in the rostrolateral and anterior visual cortex, blurring the functional distinction between primary and higher visual areas. Thus, optic flow representations in specific areas of the visual cortex rely on binocular integration of motion information from the retina.

neuroscience

EyeLoop: An open-source, high-speed eye-tracker designed for dynamic experiments

Eye-tracking is a method for tracking the position of the eye and size of the pupil, often employed in neuroscience laboratories and clinics. Eye-trackers are widely used, from studying brain dynamics to investigating neuropathology and disease models. Despite this broad utility, eye-trackers are expensive, hardware-intensive, and proprietary, which have limited this approach to high-resource facilities. Besides, experiments have largely been confined to static open-loop designs and post hoc analysis due to the inflexibility of current systems. Here, we developed an open-source eye-tracking system, named EyeLoop, tailored to dynamic experiments. This Python-based software easily integrates custom functions via a modular logic, tracks a multitude of eyes, including rodent, human, and non-human primate eyes, and it operates well on inexpensive consumer-grade hardware. One of the most appealing applications of EyeLoop is closed-loop experiments, in which the eyes evoke stimulus feedback, such as rapid neuronal optogenetic stimulation. By using EyeLoop, we demonstrate its utility in an open-loop, a closed-loop, and a biomedical experiment. With a remarkably low minimal hardware cost amounting to 29 USD, EyeLoop makes dynamic eye-tracking accessible to low-resource facilities, such as high schools, small laboratories, and small clinics.

neuroscience