DOI: 10.5061/dryad.2280gb5tx
title: Data for: Hippocampal place codes are gated by behavioral engagement

description: As animals explore an environment, the hippocampus is thought to
 automatically form and maintain a place code by combining sensory and
 self-motion signals. Instead, we observed an extensive degradation of the
 place code when mice voluntarily disengaged from a virtual-navigation
 task, remarkably even as they continued to traverse the identical
 environment. Internal states therefore can strongly gate spatial maps and
 reorganize hippocampal activity even without sensory and self-motion
 changes.
description: Mice were trained in virtual
 reality to navigate a two
 meter-long linear track that repeated in a circular topology. Mice
 received liquid rewards if they licked a spout in a 20-cm long reward
 zone, whereas licks in other parts of the track were
 unrewarded.
 We measured the activity of
 hundreds of CA1 neurons using cellular-resolution calcium imaging with
 jRGECO1a or jGCaMP8m. 
description: This dataset contains calcium imaging data from the hippocampus
 in mice performing a linear-track navigation task in virtual reality. Mice
 needed to lick within the reward zone on each trial to trigger water
 reward delivery, and we used licking behavior to analyze the spontaneous
 changes in their internal states and spatial representations while the
 task environment and reward contingencies remained constant. 
 The data include 39 sessions from 9 mice that express either
 jRGECO1a or jGCaMP8m.  All 39 sessions are from mice
 that exhibit good behavioral performance and satisfactory imaging quality.
 “session_info.mat” contains high-level information on the sessions and its
 content is described below.  jrgeco_mice: a
 list of mice that were imaged with jRGECO1a.
 gcamp_mice: a list of mice that were imaged with
 jGCaMP8m. beha_im_sessions: a list of all 39 sessions
 that have good behavior and imaging.
 beha_im_clusters: cluster labels of all trials based on lick
 behavior (lick rate and selectivity; see Methods in paper) using kmeans
 clustering with 2 clusters. Note that these the cluster labels ONLY
 correspond to trials in valid_trial_N for each sessions and may therefore
 be smaller in size than the total number of trials saved. 
 eng_clust_idx: cluster label that corresponds to trials with
 higher lick rate and selectivity.
 kmeans_include_sessions: a list of 32 sessions that include more
 than 10 trials of both clusters. These sessions are analyzed primarily in
 the paper. kmeans_include_cluster: same as
 beha_im_clusters, but only for the 32 sessions in
 kmeans_include_sessions.  Session names
 are in the format "mouse_yyyymmdd" and each session has one
 "_behavior.mat" file and one "_neural.mat"
 file.  The "_neural.mat" file for all
 sessions include the deconvolved and smoothed activity as “deconv_sm.” The
 32 sessions in kmeans_include_sessions additionally include the raw ∆F/F
 activity as “dff.” These activity files have shape # neurons X # imaging
 frames (denoted as nFrames below). The
 "_behavior.mat" file contains behavioral information on each
 imaging frame in “iter” and at the trial level in “trial”. “Valid_trial_N”
 denote the trials that are of normal duration (between 3 and 60 seconds)
 and excludes trials whose licking was not indicative of the mouse’s
 internal states (crutch trials where licks only occurred after the reward
 was delivered within the reward zone; see Methods). See
 below for detailed descriptions.  *iter*
 rawMovement: [3×nFrames double] pitch; roll; yaw data
 from ball sensors position: [4×nFrames double]
 X,Y,Z,Theta (heading direction) in VR world. Note that X,Z,Theta are
 fixed. Units are Virmen Units.  2 Virmen units = 1 cm.
 velocity: [4×nFrames double] dX,dY,dZ,dTheta. 
 tN: [1×nFrames double] trial number. corresponds to N in trial
 structure. Note that imaging may not be started right away after virtual
 reality is displayed and may also end earlier than virtual reality.
 Therefore, tN  may only be a subset of N in trial structure. 
 reward: [1×nFrames double] reward given on frame. Values in
 units of micro liter.  isLick: [1×nFrames double]
 binary indicator of lick on each imaging frame 
 isVisible: [1×nFrames double] binary indicator of whether the
 virtual world is visible or not. 0 means the world is dark and no visual
 cues are displayed.  manualReward: [1×nFrames double]
 manual reward given *trial*
 N: trial number. duration: trial
 duration in seconds.  totalReward: total reward on
 that trial in micro liters. isProbe: whether the
 trial a probe trial (unrewarded regardless of behavior; see task
 description). isCrutch: whether trial is crutch trial
 (rewarded regardless of behavior).  totalLicks: total
 number of licks on trial. fractionVisible: fraction
 of the trial in which the world is visible. 
 meanSpeed: mean forward running speed on trial in VR
 units/s.