Publications by authors named "Elliott Abe"

Animals move their head and eyes as they explore the visual scene. Neural correlates of these movements have been found in rodent primary visual cortex (V1), but their sources and computational roles are unclear. We addressed this by combining head and eye movement measurements with neural recordings in freely moving mice.

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Visual input during natural behavior is highly dependent on movements of the eyes and head, but how information about eye and head position is integrated with visual processing during free movement is unknown, as visual physiology is generally performed under head fixation. To address this, we performed single-unit electrophysiology in V1 of freely moving mice while simultaneously measuring the mouse's eye position, head orientation, and the visual scene from the mouse's perspective. From these measures, we mapped spatiotemporal receptive fields during free movement based on the gaze-corrected visual input.

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In natural contexts, sensory processing and motor output are closely coupled, which is reflected in the fact that many brain areas contain both sensory and movement signals. However, standard reductionist paradigms decouple sensory decisions from their natural motor consequences, and head-fixation prevents the natural sensory consequences of self-motion. In particular, movement through the environment provides a number of depth cues beyond stereo vision that are poorly understood.

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Many studies of visual processing are conducted in constrained conditions such as head- and gaze-fixation, and therefore less is known about how animals actively acquire visual information in natural contexts. To determine how mice target their gaze during natural behavior, we measured head and bilateral eye movements in mice performing prey capture, an ethological behavior that engages vision. We found that the majority of eye movements are compensatory for head movements, thereby serving to stabilize the visual scene.

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Performing a stereotyped behavior successfully over time requires both maintaining performance quality and adapting efficiently to environmental or physical changes affecting performance. The bird song system is a paradigmatic example of learning a stereotyped behavior and therefore is a good place to study the interaction of these two goals. Through a model of bird song learning, we show how instability in neural representation of stable behavior confers advantages for adaptation and maintenance with minimal cost to performance quality.

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