Natural binocular depth discrimination behavior in mice explained by visual cortical activity.

Natural binocular depth discrimination behavior in mice explained by visual cortical activity.
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DOI:
10.1016/j.cub.2021.02.031
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发表时间:
2021-05-24
期刊:
Current biology : CB
影响因子:
--
通讯作者:
McGee AW
McGee AW
中科院分区:
其他
文献类型:
--
作者:
Boone HC;Samonds JM;Crouse EC;Barr C;Priebe NJ;McGee AW

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In mice and other mammals, forebrain neurons integrate right and left eye information to generate a three-dimensional representation of the visual environment. Neurons in the visual cortex of mice are sensitive to binocular disparity, yet it is unclear whether that sensitivity is linked to the perception of depth. We developed a natural task based on the classic visual cliff and pole descent tasks to estimate the psychophysical range of mouse depth discrimination. Mice with binocular vision descended to a near (shallow) surface more often when surrounding far (deep) surfaces were progressively more distant. Occlusion of one eye severely impaired their ability to target the near surface. We quantified the distance at which animals make their decisions to estimate the binocular image displacement of the checkerboard pattern on the near and far surfaces. Then we assayed the disparity sensitivity of large populations of binocular neurons in primary visual cortex (V1) using two-photon microscopy, and quantitatively compared this information available in V1 to their behavioral sensitivity. Disparity information in V1 matches the behavioral performance over the range of depths examined and was resistant to changes in binocular alignment. These findings reveal that mice naturally use stereoscopic cues to guide their behavior and indicate a neural basis for this depth discrimination task. Boone et al. estimate the psychophysical range of depth discrimination for mice with a modified cliff task. They demonstrate mice require binocular vision to perform the task and identify that disparity information from populations of neurons in visual cortex is sufficient to predict performance across a range of depths.
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