#Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex This data is associated with the paper “Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex” in the Journal of Neuroscience. We presented audiovisual stimuli (visual drifting gratings paired with auditory white noise) to awake mice, while performing extracellular electrophysiological recordings from neurons in the primary visual cortex. We analyzed the neuronal responses to see how they respond to visual inputs with and without sound. The raw neuronal data is a product of Cheetah Neuralynx software converted to .mat Matlab files (see “Raw Data” below), and then subsequently processed using Matlab software (see “Code for processing raw data”). Code for generating the figures found in the paper is also included (see “Code for generating figures”). #Raw data The “Raw data” folder, which is organized by mouse and recording date. The mouse subjects included in the study are: AW117, AW118, AW121, AW124, AW157, AW158, AW159, AW162, AW163, AW64, AW165. Each mouse subject has two subfolders, labeled according to the recording date, except for AW164 which has one. Each recording folder (eg. AW117 -> 20200221-1) contains the raw Kilosort2 data for each sorted neuron (found in “SpikeMat” folder). Each neuron’s .mat file in this subfolder is a structure with information about the neuron (“CellInfo”), and the frames of the recording at which the neuron spiked (“SpikeData”, fourth row) measured in microseconds relative to the start of the recording. The “Events” variable is when each audiovisual stimulus trial was presented. The recording folder also contains information about that recording session’s stimulus (“StimInfo” folder). Each recording folder also contains a folder “AVMultiContrastDriftingGratingsWhiteNoise,” which contains neuronal population data from this session (see “Code for processing raw data” below). Population data is stored as “AVMultiContrastDriftingGratingsWhiteNoiseData\_selectivity.mat”, with structures identifying stimulus-responsive neurons as well as the analysis parameters. Population tuning curves, averaged across each neuron in the session, are represented in “Neuron-wise orientation preference.fig” and “Population orientation preference.fig”. The “Figures” subfolder has .fig and .jpg files showing each neuron’s tuning curve, raster plots, and coefficient of variance. For mouse subjects AW159-165, video data was also acquired during each recording session. This data is found under the recording folder (eg AW159 -> 20201213-1) in the “Video data” folder, containing the frame and time stamps of each trial for the stimulus during this recording, as well as the preprocessed movement data (“movementData.mat”, see “Code for processing raw data” below). In each “Video data” folder, “AVMultiContrastDriftingGratingsWhiteNoise\_GLM\_TimeCourse” subfolder contains GLM-estimated PSTHs for each neuron in the recording (see “Code for generating figures” below). #Code for processing raw data The “Code for processing raw data” folder contains the Matlab scripts for generating raster plots, PSTHs, and tuning curves from the raw data. Scripts additionally categorize neurons as light-responsive, sound-modulated, orientation/direction-selective, and/or movement-modulated. The folder also contains a Matlab script for preprocessing of video data (relevant for mouse subjects AW159-165). The files included in this folder include: AVMultiContrastDriftingGratingsWhiteNoise\_Analysis.m AVMultiContrastDriftingGratingsWhiteNoise\_Video\_Analysis.m RawVideoProcessing.m The outputs from each of these scripts are saved in the associated “Raw data” subfolders (see “Raw data” above). #Code for generating figures The “Code for generating figures” folder contains the Matlab scripts that perform additional analyses across recording sessions and generates appropriate figures to be included in the manuscript. The files included in this folder include: AVMultiContrastDriftingGratingsWhiteNoise\_DepthHistogram.m (Figure 3) AVMultiContrastDriftingGratingsWhiteNoise\_GLM\_GLMcoeffDepth.m (Figure 7) AVMultiContrastDriftingGratingsWhiteNoise\_GLM\_timeAnalysis.m (Figure 7) AVMultiContrastDriftingGratingsWhiteNoise\_MetaAnalysis.m (Figure 5) AVMultiContrastDriftingGratingsWhiteNoise\_MetaAnalysis2.m (Figure 3, 4) AVMultiContrastDriftingGratingsWhiteNoise\_MLEIndividualUnits.m (Figure 8) AVMultiContrastDriftingGratingsWhiteNoise\_MLErandom\_locoWhisk.m (Figure 10) AVMultiContrastDriftingGratingsWhiteNoise\_MLErandom\_pairwise.m (Figure 9) AVMultiContrastDriftingGratingsWhiteNoise\_MLErandom.m (Figure 9) AVMultiContrastDriftingGratingsWhiteNoise\_TimeWindowInformation.m (Figure 2) AVMultiContrastDriftingGratingsWhiteNoise\_tuningCurve.m (Figure 4) AVMultiContrastDriftingGratingsWhiteNoise\_Video\_postAnalysis.m (Figure 6) GLMTimeWindow\_PostAnalysis.m (Figure 7) #Stimuli The “Stimuli” folder contains the Matlab script that was used to generate the audiovisual stimulus for each recording session: ephys\_AV\_driftingGratingsMultContrast\_whiteNoise.m