DOI: 10.5061/dryad.8cz8w9gmw
title: Dynamics of gaze control during prey capture in freely moving mice

description: Most studies of visual processing are conducted under head- and
 gaze-restricted conditions. While this provides experimental control, it
 radically limits the natural exploration of the visual world which is
 typically achieved through directed eye, head, and body movements. As
 such, less is known about how animals naturally sample the external visual
 world to acquire relevant visual information in natural
 contexts. To determine how mice target their gaze and sample the
 visual world during natural behavior, we measured head and bilateral eye
 movements in mice performing prey capture, an ethological behavior that
 engages vision. We find that most eye movements are compensatory for head
 movements but that non-compensatory movements occur during head turns.
 Importantly, we find that non-compensatory gaze shifts (i.e.,
 saccades) do not target a discreet location in visual
 space (e.g., the prey location), but that orienting movements are driven
 by the head and work to sequentially shift and recenter the visual
 field. Data shared here include simultaneous recordings of eye
 and head movements from 105 trials of prey capture behavior across 7
 animals. All data are available as .mat files. 
description: In these experiments freely behaving mice (3 males, 4 females)
 hunted live crickets in a rectangular plexiglass arena. Mice were equipped
 with two miniaturized cameras that recorded eye movements from each eye
 and an accelerometer coupled with a gyroscope that recorded head movements
 in three dimensions. Additionally, an overhead camera recorded the
 behavior of the mouse and cricket.  Data from the two
 eyes was collected at 30 fps (frames per second) in NTSC format. This
 format is an interlaced format in which two sequential images acquired at
 60fps are interdigitized into each frame on alternate horizontal lines. We
 de-interlaced the video in order to restore the native 60fps by separating
 out alternate lines of each image. Then, we linearly downsampled the
 resolution by a factor of two in order to match spatial resolution in
 horizontal and vertical dimensions. <br> <br> DeepLabCut was
 used to extract 8 points around the pupil from both eye tracking
 recordings. An ellipse was fit to these 8 points and the rotation of this
 ellipse was converted into angular rotations. DeepLabCut was also used to
 extract 8 points on the head of the mouse (nose, center of left camera
 implant, end of left camera implant, left ear, center of right camera
 implant, end of right camera implant, right ear, center of back of
 head) and two points on the cricket (head and body). The 8 points on the
 mouse's head were used to compute the horizontal angle of the
 mouse's head as well of the azimuth of the mouse's head relative
 to the cricket.  Approaches were defined as times at
 which the velocity of the mouse was greater than 1cm/sec, the azimuth of
 the mouse relative to the cricket was between -45 and 45 degrees, and
 distance to the cricket was decreasing at a rate greater than 10
 cm/sec. <br>  
description: DeepLabCut occassionally produced errors in estimating points on
 the eyes or head of the mouse or cricket. DeepLabCut returns these points
 with low-likelihood values. Rather than analyzing these faulty points, we
 have set them to NaNs.