DOI: 10.5061/dryad.t1g1jwt6d
title: Data for: Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex

description: Cortical circuits contain multiple types of inhibitory neurons which shape
 how information is processed within neuronal networks. Here, we asked
 whether somatostatin-expressing (SST) and vasoactive intestinal
 peptide-expressing (VIP) inhibitory neurons may have distinct effects on
 population neuronal responses for noise bursts of varying intensities. We
 stimulated optogenetically SST or VIP neurons while simultaneously
 measuring the calcium responses of populations of hundreds of neurons to
 sounds. Upon SST neuronal activation, noise burst representations became
 more discrete for different intensity levels, relying on cell identity
 rather than strength. In contrast, upon VIP neuronal activation, noise
 bursts of different intensity levels activated overlapping neuronal
 populations, albeit at different response strengths. At the single-cell
 level, SST and VIP neuronal activation differentially modulated the
 response-level curves of monotonic and nonmonotonic neurons. SST neuronal
 activation was consistent with a shift of the neuronal population
 responses toward a more localist code with different cells responding to
 sounds of different intensities. By contrast, VIP neuronal activation
 shifted responses towards a more distributed code, in which sounds of
 different intensity levels are encoded in the relative response of similar
 cells. These results delineate how distinct inhibitory neurons in the
 auditory cortex may dynamically control cortical population codes.
 Different inhibitory neuronal populations may be recruited under different
 behavioral demands, depending on whether categorical or invariant
 representations are advantageous for the task.
description: # Data for: Distinct inhibitory neurons differently shape neuronal codes
 for sound intensity in the auditory cortex --- Readme for the description
 of datasets and data analysis for the paper titled "Differential
 modulation of cortical codes for sounds of varying intensity by distinct
 inhibitory neurons" by Tobin, Sheth, Wood, and Geffen. ## Description
 of the data and file structure ### Data Folders Data: Folder SST
 Experimental data for the mice SST-Cre x Cdh23 injected with GCaMP7f +
 Flex.ChrimsonR.tdTomato Each dataset is a recording from a different
 population of neurons in AC Data: Folder VIP Experimental data for the
 mice VIP-Cre x Cdh23 injected with GCaMP7f + Flex.ChrimsonR.tdTomato Each
 dataset is a recording from a different population of neurons in AC Data:
 Folder Control_VIP Control data with the VIP cells in mice VIP-Cre x Cdh23
 injected with GCaMP7f + Flex.tdTomato Each dataset is a recording from a
 different population of neurons in AC Data: Folder Control_NonVIP Control
 data with the Non-VIP cells in mice VIP-Cre x Cdh23 injected with GCaMP7f
 + Flex.tdTomato Each dataset is a recording from a different population of
 neurons in AC ### Dataset Types #### SingleCellData Emplacement: in
 folders SST ; VIP ; Control_VIP ; Control_NonVIP There is one dataset per
 recording. Each recording contains: * fr : imaging frame rate in Hz *
 raster_ordered : raster of the fluorescence for each trial as a function
 of frame number (=time) for each neuron in the population, ordered by
 combination of laser power and sound level (example: trials for
 combination 1: raster_ordered(1:10,:,:); trials for combination 5:
 raster_ordered(51:60,:,:) ... ) size is (210 x 181 x Ncells) with 210 the
 number of different combinations of laser and sound (21 combinations)
 times the number of trials (=repeats) per combination (10 repeats); 181
 the number of frames (~ 30 frames/sec * 5 sec); Ncells the number of
 neurons in the population * rasterInfo : structure containing the
 information about the construction of raster_ordered from the original
 fluorescence trace rasterInfo.preOnsetTime : duration in seconds before
 stimulus onset (in raster_ordered, mean_Trace, std_mean_Trace)
 rasterInfo.postOnsetTime : duration in seconds after stimulus onset (in
 raster_ordered, mean_Trace, std_mean_Trace) rasterInfo.doDFoverF0 : if
 equal to 1, do baseline correction. For each trial, the average over the
 baseline (duration preOnsetTime) is subtracted from the fluorescence
 trace, and the result is normalized by the standard deviation of the
 baseline (in raster_ordered, mean_Trace, std_mean_Trace) * mean_Trace :
 fluorescence trace averaged over the different repeats of a laser+sound
 combination as a function of frame number (=time) for each neuron in the
 population (average of raster_ordered over the trials for each sound+laser
 combination) size is (21 x 181 x Ncells) with 21 the number of different
 combinations of laser (3 powers) and sound (7 levels); 181 the number of
 frames (~ 30 frames/sec * 6 sec); Ncells the number of neurons in the
 population * std_mean_Trace : standard deviation of the fluorescence trace
 over the different repeats of a laser+sound combination as a function of
 frame number (=time) for each neuron in the population (standard deviation
 of raster_ordered over the trials for each sound+laser combination) size
 is (21 x 181 x Ncells) with 21 the number of different combinations of
 laser (3 powers) and sound (7 levels); 181 the number of frames (~ 30
 frames/sec * 6 sec); Ncells the number of neurons in the population *
 exptInfo : structure containing the following experimental information:
 exptInfo.mouse : experimental mouse name exptInfo.recDate : recording date
 exptInfo.Filename : original file name exptInfo.mouseline : experimental
 mouse line * RedCells : structure containing the information about the
 identification of the "red" neurons (VIP or SST depending on
 mouseline) from the 2p recording RedCells.Threshold : threshold in
 standard deviations (sigma, see Methods) above which a cell is identified
 as "red" RedCells.AvGrayValue_red : Average value in standard
 deviations of the fluorescence of each neuron in the red channel
 RedCells.isCell_red : Array with 1 if a neuron is identified as a
 "red" cell (AvGrayValue_red above Threshold), and 0 otherwise
 RedCells.SourceData : original source data for the fluorescence
 RedCells.AnalysisFile : original analysis file for red cell identification
 RedCells.bleedthrough_fitcoeff : bleedthrough coefficient from the green
 channel to the red channel #### StimInfo: structure containing information
 about the stimulus: stimInfo.fs : stimulus sampling frequency in Hz
 stimInfo.tDur_opto : Duration in sec that laser stimulus is on overall
 stimInfo.tOn_opto : Duration in sec of the "on" part of the
 laser pulse during the laser stimulus stimInfo.tOff_opto : Duration in sec
 of the "off" part of the laser pulse during the laser stimulus
 stimInfo.ISI_opto : Interstimulus interval in sec between two laser
 stimuli stimInfo.amplitude_opto : laser powers as the ratio of maximum
 power (see Methods for calibration) stimInfo.nPulses : number of laser
 pulses during a laser stimulus stimInfo.delay_OptoTone : delay in seconds
 between the onset of the laser stimulus and the onset of the sound
 stimulus stimInfo.index : combination of sound level (in dB, first column)
 and laser power (in the ratio of maximum power, second column)
 stimInfo.index_names : names of columns for index stimInfo.order : order
 of presentation of the different laser+sound combinations (row of index)
 stimInfo.noiseDur : duration in milliseconds of a noise burst
 stimInfo.nClicks : number of clicks during a noise burst
 stimInfo.clickRate : frequency in Hz of the clicks stimInfo.intensity :
 sound pressure levels in dB stimInfo.ISI : the interstimulus interval
 between two sound stimuli stimInfo.repeats : number of repeats per
 sound+laser combination #### SingleCellFits Emplacement: in folders SST;
 VIP; This is the output from the fitting of the rate-level curves (code
 for fitting in singeCellFittingCode). #### Sigmoid fits sigmoidFit* :
 contains the parameters to the single-cell fits to the rate-level curves
 from all recordings with: sigmoidNoLight* : set of parameters of cells fit
 with a sigmoid function at no laser power sigmoidMidLight* : set of
 parameters of cells fit with a sigmoid function at medium laser power
 sigmoidHighLight* : set of parameters of cells fit with a sigmoid function
 at high laser power The set of parameters are : sigmoid*(:,1) : recording
 number - 1 (python indexing) sigmoid*(:,2) : cell number in recording -1
 (python indexing) sigmoid*(:,3) : y_0 sigmoid*(:,4) : y_0 + y_range
 sigmoid*(:,5) : x_0 sigmoid*(:,6) : Delta x (with no constraint on sign)
 sigmoid*(:,7) : interpolated error #### Gaussian fits gaussianFit* :
 contains the parameters to the single-cell fits the rate-level curves from
 all recordings with: gaussNoLight* : set of parameters of cells fit with a
 Gaussian function at no laser power gaussMidLight* : set of parameters of
 cells fit with a Gaussian function at medium laser power gaussHighLight* :
 set of parameters of cells fit with a Gaussian function at high laser
 power The set of parameters are : gauss*(:,1) : recording number - 1
 (python indexing) gauss*(:,2) : cell number in recording -1 (python
 indexing) gauss*(:,3) : y_range gauss*(:,4) : x_mean gauss*(:,5) : sigma
 gauss*(:,6) : y_0 gauss*(:,7) : interpolated error ## Code/Software ####
 Set up In the code scripts, replace with local folder location for
 datasets in: * AnalysisScript_DataLoad * AnalysisScript_Fig4 *
 AnalysisScript_Fig5 Code for functions used in AnalysisScript_DataLoad is
 in Code\functions Code for fitting the rate-level curves of single cells
 is in the folder Code\singleCellFittingCode\ (the datasets of the fit
 parameters are available so no need to re-run this code to plot the
 figures) #### Dataset selection and plotting figures In
 AnalysisScript_DataLoad.m, replace line 11 of the code with the chosen
 dataset: SST, VIP, Control_VIP, or Control_NonVIP. Start by compiling
 Analysis_DataLoad.m with a chosen dataset (SST, VIP, Control_VIP, or
 Control_NonVIP) then compile the code for a chosen figure. The code for
 plotting the figures is in AnalysisScript_* from Fig1 to Fig6.