DOI: 10.5061/dryad.bk3j9kdm6
title: Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals

description: In classical cerebellar learning, Purkinje cells (PkCs) associate climbing
 fiber (CF) error signals with predictive granule cells (GrCs) active just
 prior (~150ms). Cerebellum also contributes to behaviors characterized by
 longer timescales. To investigate how GrC-CF-PkC circuits might learn
 seconds-long predictions, we imaged simultaneous GrC-CF activity over days
 of forelimb operant conditioning for delayed water reward. As mice learned
 reward timing, numerous GrCs developed anticipatory activity ramping at
 different rates until reward delivery, followed by widespread time-locked
 CF spiking. Relearning longer delays further lengthened GrC activations.
 We computed CF-dependent GrC→PkC plasticity rules, demonstrating that
 reward-evoked CF spikes sufficed to grade many GrC synapses by
 anticipatory timing. We predicted and confirmed that PkCs could thereby
 continuously ramp across seconds-long intervals from movement to reward.
 Learning thus leads to new GrC temporal bases linking predictors to remote
 CF reward signals—a strategy well-suited to learn to track long intervals
 common in cognitive domains.
description: # A cerebellar granule cell–climbing fiber computation to learn to track
 long time intervals
 [https://doi.org/10.5061/dryad.bk3j9kdm6](https://doi.org/10.5061/dryad.bk3j9kdm6) Data files (stored in matlab (.mat) format) are used to produce main and supplementary figures. The description of all variables is as follows: For each mouse on each session, there is a data structure with many fields, some of which are present only contextually for some session types. Call the current structure session’s data structure "curd" "curd" contains many fields, among them: • nIC_GrC, nIC_CF - number of GrCs or CFs • pixh, pixw - image height and width in pixels • ICmat_CF, ICmat_GrC – Npixh X Npixw X Ngrc or Ncf • dtb, dtimCb, dtDLC - sampling time step in sec for NIdaq behavioral data, imaging data, and behavioral video data • ntb, ntimCb - total # of samples; NIdaq or microscope • nc - total number of detected forelimb movements • "midAlgn" and "rewAlgn." Each of these fields is in turn another structure • Several variables are acquired on the NIdaq and are of length ntb ``` lick - binary lick sensor contacts frameCb - imaging frame counter pos - ntb X 2 matrix of x-y handle positions sol - binary solenoid gate ``` • Several variables are based on the microscope acquisiton and have length ntimCb ``` sigFilt_GrC, sigFilt_CF, spMat_CF - nIC_GrC X ntimCb or nIC_CF X ntimCb matrix of z-scored cell fluorescence or binary spike trains ``` • goodmvdir - nc X 1 binary vector, 1 for movements that passed start/mid/end/reward alignment tests • rewtimes, truestart, midpt, trueend - detected times of reward, and movement start, middle, and end • valid_lick_trials, goodDLC - nc X 1 vector, 1 for trials where lick or camera data passed QC • tmpxx, tmpxCb, tmpxDLCnew - vector of time stamps centered on 0 sec with sample interval dtb, dtimCb, or dtDLC curd.midAlgn/startAlgn/rewAlgn fields • sigFilt_GrC, sigFilt_CF, sp_CF - [nc X nIC X nt] matrix of GrC or CF zscored fluorescence or CF spikes, trials-by-cells-by-timepoints per trial, nt corrresponds to lengths of tmpxCb ``` Center of time axis is 0s wrt movement midpoint, start point, or reward time for midAlgn, startAlgn, rewAlgn respectively ``` • sol - nc X nt matrix of binary solenoid openings • lick - same but for binary lick sensor contacts • pos Nmv X 2 X Nt_b same but for X and Y handle position ## Code/Software Corresponding code to produce figures from these data can be found in [https://github.com/wagnerlabnih/garcia-garcia-neuron-2024](https://github.com/wagnerlabnih/garcia-garcia-neuron-2024)