DOI: 10.5061/dryad.v6wwpzh5t
title: Data from: Learning enhances behaviorally relevant representations in apical dendrites

description: Learning alters cortical representations and improves perception. Apical
 tuft dendrites in Layer 1, which are unique in their connectivity and
 biophysical properties, may be a key site of learning-induced plasticity.
 We used both two-photon and SCAPE microscopy to longitudinally track
 tuft-wide calcium spikes in apical dendrites of Layer 5 pyramidal neurons
 in barrel cortex as mice learned a tactile behavior. Mice were trained to
 discriminate two orthogonal directions of whisker stimulation.
 Reinforcement learning, but not repeated stimulus exposure, enhanced tuft
 selectivity for both directions equally, even though only one was
 associated with reward. Selective tufts emerged from initially
 unresponsive or low-selectivity populations. Animal movement and choice
 did not account for changes in stimulus selectivity. Enhanced selectivity
 persisted even after rewards were removed and animals ceased performing
 the task. We conclude that learning produces long-lasting realignment of
 apical dendrite tuft responses to behaviorally relevant dimensions of a
 task.
description: Movies of apical dendrites were acquired by imaging genetically
 encoded calcium indicator with two-photon microscopy (planar imaging) or
 SCAPE microscopy (volumetric imaging). Microscopes were controlled by
 ScanImage in the case of two-photon or custom software in the case of
 SCAPE. Movies were motion corrected and segmented using CaImAn.
description: # Data from: Learning enhances behaviorally relevant representations in
 apical dendrites
 [https://doi.org/10.5061/dryad.v6wwpzh5t](https://doi.org/10.5061/dryad.v6wwpzh5t) ## Description of the data and file structure The goal of these experiments was to compare the responsiveness and selectivity of layer 1 apical tuft dendrites in barrel cortex before and after head-fixed mice learned a direction discrimination task. ### Files and variables #### File: ApicalResponses.xlsx **Description:** the overall responsiveness of apical tufts before, during, and after learning. Each row corresponds to one segmented apical tuft. ##### Variables * *CS+* is the magnitude of the response (in fractional delta F/F0) for the rewarded direction * *CS-* is the magnitude of the response (in fractional delta F/F0) for the unrewarded direction * *Conditioning day* is the number of a session #### File: ApicalSelectivity2p.xlsx **Description:** the selectivity of individual apical tufts before, during, and after learning. Each row corresponds to one segmented apical tuft imaged by two-photon microscopy. ##### Variables * *CS+* is the magnitude of the response (in fractional delta F/F0) for the rewarded direction * *CS-* is the magnitude of the response (in fractional delta F/F0) for the unrewarded direction * *Stim1* and *Stim2* are the magnitude of responses for the 2 directions when neither are rewarded (repeated exposure group) * Data in Fig3c,d worksheets are histograms of the selectivity index of all apical tufts on the indicated session. The bin size was 0.2, and the *bin centers* are the midpoint between each bin edge - e.g. the bin center of 0.9 corresponds to values &gt; 0.8 and &lt;= 1.0. *Proportion* is the fraction of all tufts with a selectivity index in a given bin. #### File: ApicalSelectivitySCAPE.xlsx **Description:** the selectivity of individual apical tufts before, during, and after learning. Each row corresponds to one segmented apical tuft imaged by SCAPE microscopy. ##### Variables * *CS+* is the magnitude of the response (in fractional delta F/F0) for the rewarded direction * *CS-* is the magnitude of the response (in fractional delta F/F0) for the unrewarded direction * The bin size was 0.2, and the *bin centers* are the midpoint between each bin edge - e.g. the bin center of 0.9 corresponds to values &gt; 0.8 and &lt;= 1.0. *Proportion* is the fraction of all tufts with a selectivity index in a given bin. ## Code/software We used commercial software (Matlab 2020-2023) to analyze data, but these data can be viewed in any free software that can load .xls files and generate standard graphical plots (e.g., Python, R, etc).