DOI: 10.5061/dryad.f7m0cfzb1
title: Data from: Thalamic reticular neurons provide cell type-specific modulation of sound processing in the auditory thalamus

description: Inhibition along the auditory pathway is crucial for the processing of
 acoustic information. Within the auditory thalamus, a key region in the
 central auditory pathway, inhibition is provided by the thalamic reticular
 nucleus (TRN), comprised of two large classes of inhibitory neurons,
 parvalbumin (PVTRN) and somatostatin (SSTTRN) positive.  In the
 auditory cortex, PV and SST neurons differentially shape auditory
 processing. We found that the ventral MGB, the thalamic region in the
 direct ascending auditory pathway, receives inputs predominantly from
 PVTRN neurons, whereas SSTTRN neurons project to the dorso-medial regions
 of MGB. Consistently, inactivating PVTRN neurons increased sound-evoked
 activity in over a third of neurons in the vMGB, with another large
 fraction of neurons being suppressed. By contrast, inactivating SSTTRN
 neuronal activity largely reduced tone-evoked activity in vMGB neurons.
 Cell type-specific computational models revealed candidate circuit
 mechanisms for generating the bidirectional effects of TRN inactivation on
 MGB sound responses.  These differential inhibitory pathways
 within the auditory thalamus suggest a cell-specific role for thalamic
 inhibition in auditory computation and behavior.
description: # Data from: Thalamic reticular neurons provide cell type-specific
 modulation of sound processing in the auditory thalamus ## Instructions *
 Extract Data.zip. - Place the data folder in the same directory as the
 'Paper_code' Python scripts. * Follow the instructions in the
 code repository README to start the Python code notebooks. - Ensure the
 exact name of the data folder matches the folder name under the
 'Loading Data' section, `dataLoc` variable. * The correct virus
 and recording data will then be loaded when the scrthe ipt runsthe. ##
 Directory Structure ``` PV_Recordings/ SOM_Recordings/ Stimuli/
 TRN_SST_recording/ probe_depth_per_recording.xlsx ``` Probe depth
 probe_depth_per_recording.xlsx * recording probe depth per session for
 aligning cell to actual depth within MGB. * First column: recording
 session name * Second column: probe depth in µm extracted from the
 Scientifica software; zero at brain surface. #### PV_Recordings: Data
 files used in PV-Cre mice.  Contains folders with data from experiments
 performed in PV-Cre mice. Within this folder there are two sub-folders for
 control data (Controls) and experimental data (stGtACR1). Each one of
 these sub-folders contain a folder labeled Data_Paper. Data_Paper contains
 another subfolder named PSTH: which contains the peri-stimulus time
 histogram datasets for both stimuli presented during experiments, tuning
 curve and laser only presentation. This dataset will be used when running
 the Python notebook labeled
 "SpikeAnalysis_All_Mice_PaperCode_Final2" and contains
 pre-processed data (ie noise clusters removed, etc.). The other subfolders
 within Data_Paper are the data folders for each mouse ID. Within each
 mouse ID folder are folders for the individual session ID's, each of
 which contain a folder named "data" that has the raw data for
 each recording session. This dataset can be used with the Python notebook
 labeled "QuickSavePSTH_Paper" or
 "QuickSavePSTH_Laser_Paper." #### SOM_Recordings: Data files
 used in SST-Cre mice. Contains folders with data from experiments
 performed in SST-Cre mice. This data set has the same folder structure as
 described in PV_Recordings. #### Stimuli/ Tone stimuli data
 Stimuli/TuningCurve_50ms_Laser_50ms_Frequencies3_80Hz_02232022_stimInfo.mat Stimuli were generated using custom MATLAB code and were sampled at 200kHz with 32-bit resolution. To assess frequency response functions in neuronal populations, we generated a set of 19 pure tones of logarithmically spaced frequencies racosine-squared3kHz and 80kHz at 70dB sound pressure level relative to 20 uPa (SPL). Each tone was 50ms long with a 1-ms cosine squared ramp, repeated 40 times with an inter-stimulus-interval of 300ms and pseudo-random order. 20 of those 40 tone repetitions were accompanied by a continuous light pulse, otherwise below 1-ms cosine squared ramp starting at tone onset and ending at tone offset (50ms; for details on light stimuli see Below). Laser stcosine-squaredmuli/LaserStim_Optotag_50ms_400ISI_5Reps_stimInfo.mat On light-Only trials, we delivered continuous (1-ms cosine squared ramp) lightpulses of different durations (10ms, 25ms, 50ms, and 100 ms) through an implanted fiber-optic cannula via a fiber-coupled blue LED (460nm, Prizmatix, Optogenetics-LED-Blue). During sound presentation,on light-On trials, we delivered a continuous 50-ms pulse with a 1-ms squared ramp concurrent with the tone stimulus. The following variables are the ones that will be extracted for analysis: * stimInfo['trialOrder'] = extracts the trial order (dimensions: frequency (Hz), laser status (0 = laser off; 1 = laser on), event pulse) * stimInfo['tDur'] = extracts the tone duration (time in seconds) * stimInfo['ITI'] = extracts theinter-triall interval (time in seconds) * stimInfo['laserDur'] = extracts laser laser-onlytime in seconds)  "LaserStim_Optotag_50ms_400ISI_5Reps_stimInfo.mat" file containing stimulus information for laser inter-trials. The following variables are the ones that will be extracted for analysis: * stimInfo['ITI'] = extracts the inter trial interval (time in seconds) * stimInfo['laserDur'] = extracts laser duration (time in seconds)  #### PV and SOM recordings folders These folders contain folders for: Controls/ * Control empty viral vector. stGtACR1/ * stGtACR1 containing viral vector. Each of these contains `Data_Paper/` folders with: PSTH/ * PSTH (Peri-Stimulus Time  Histogram) data for b, both Laser-only and tone response data, subdirectories named based on the stimulus file convention. Within the PSTH folder there are the preprocessed ".npz" files of each recording session. These files contain: - PSTH data, which is structured as (spikes in trial, time, cel, lID) - Raster data (length of cellID) - Trial data (length of cellID) - Cluster depth data (length of cellID) - Spike Sort Index which is the raster data sorted by frequency and laser condition (length of cellID) Session data S*/ * See Recording session folder structure below for details. #### TRN_SST_recordings/ PSTH data for Supplementary Figure S2. Optical inactivation of SSTTRNneurons suppresses tone-evoked activity in the TRN. #### Recording session folder structure ``` events/ messages/ cluster_group.tsv = phy label of each cluster--multi unit (mua), single unit (good), noise unit (noise) cluster_info.tsv = phy output with information about each cluster (label, amplitude, number of spikes, etc.) mean_waveforms.mat = .mat file with the extracted waveforms of each cell spike_clusters.npy spike_times.npy sync_messages.txt ``` Core Spike Data spike_times.npy * Spike times for all detected spikes. spike_clusters.npy * Cluster ID assigned to each spike. Cluster Metadata cluster_group.tsv * Contains manual curation labels from Phy. cluster_info.tsv * Per-cluster metadata and quality metrics. Waveform Data mean_wavef,orms.mat * Average waveform for each cluster. Synchronization sync_messages.txt * Synchronization log from acquisition; here it is used to extract the sampling rate of the recording. #### Subdirectories events/ Contains extracted event timestamps used to align neural data with the stimulus events. messages/ Contains logs from Kilosort processing. * channels. npy * text.npy * timestamps.npy To know more about Open. Ephys GUI manual, please check here: [https://open-ephys.github.io/gui-docs/User-Manual/Recording-data/Binary-format.html](https://open-ephys.github.io/gui-docs/User-Manual/Recording-data/Binary-format.html) ## Code/software #### Rolon_Martinez_2024 Python scripts for data analysis of Rolon Martinez et al. 2025 data Installation This code uses UV and marimo notebooks to modularize analysis scripts and handle Python version and dependencies. To get started: * Install uv * Open a terminal from this code directory. * Run marimo: uv run marimo edit - Open the script from the web UI to edit and run analysis. #### TRN_MGB_cethe lltypespecific **Mo dels to accompany Rolon-the Martinez et al.** Model USA Editollow MATLAB setup -&gt; Here,  make a new directory for simSimsion run.  Cop he src model files to the new simulation  Runectory.  Edit the Run_dsim.m template script. Visualize vm data from sims by running display_graph in the sim directory. run extractData to extract spike times and firing rate from vm data for analysis. #### SSimulationsused in the paper The simulations directory contains code files for all simulations that went into the final paper. Raw vm data results were omitted for space and exist on lab storage. #### Documentation Further information on running simulations and details of the model code can be found in the docs directory: [Run_dsim](docs/Run_dsim.md) | Script controlling variables and driving parallel simulation runs | Run_dsim.m [dsim](docs/dsim.md) | Function containing HH equations for model cells | dsim.m [simParams](docs/simParams.md) | Object containing parameters for model cells | simParams.m