DOI: 10.5061/dryad.h9w0vt4s0
title: Monkey V1/V2 neuronal population response to drifting gratings with varying orientation and contrast

description: Correlated variability in the visual cortex is modulated by stimulus
 properties. The stimulus dependence of correlated variability impacts
 stimulus coding and is indicative of circuit structure. An affine model
 combining a multiplicative factor and an additive offset has been proposed
 to explain how correlated variability in primary visual cortex (V1)
 depends on stimulus orientations. However, whether the affine model could
 be extended to explain modulations by other stimulus variables or
 variability shared between two brain areas is unknown. Motivated by a
 simple neural circuit mechanism, we modified the affine model to better
 explain the contrast-dependence of neural variability shared within either
 primary or secondary visual cortex (V1 or V2) as well as the
 orientation-dependence of neural variability shared between V1 and V2. Our
 results bridge neural circuit mechanisms and statistical models, and
 provide a parsimonious explanation for the stimulus-dependence of
 correlated variability within and between visual areas.
description: # Monkey V1/V2 neuronal population response to drifting gratings with
 varying orientation and contrast
 [https://doi.org/10.5061/dryad.h9w0vt4s0](https://doi.org/10.5061/dryad.h9w0vt4s0) These folders contain 1) electrophysiology recordings from Monkey V1 and V2 in response to drift gratings with different orientations and contrast levels 2) parameters of statistical models fit to the recordings. For details of recordings and statistical models, please check the paper [https://doi.org/10.1016/j.isci.2024.110512](https://doi.org/10.1016/j.isci.2024.110512 "Persistent link using digital object identifier")[. ](https://www.biorxiv.org/content/10.1101/2023.12.19.572428v1.)(Xia et al 2024) ## Description of the data and file structure ###### dataset/dataset1: * 105l001p16_spikes.mat * 106r001p26_spikes.mat * 106r002p70_spikes.mat * 107l002p67_spikes.mat * 107l003p143_spikes.mat This folder contains 5 .mat files. Each contains a session of simultaneously recorded neuronal population responses from V1 (recorded using Utah array) and V2 (recorded using tetrodes) to oriented grating with 8 orientations. This dataset was originally published at [https://crcns.org/data-sets/vc/v1v2-1/about_v1v2-1](https://crcns.org/data-sets/vc/v1v2-1/about_v1v2-1), here we present a mildly preprocessed version which can easily be loaded by MATLAB or using python code from [https://github.com/tinaxia2016/NeuronalVariabilityStatsModels/blob/main/stats_model_fit_dataset1.ipynb](https://github.com/tinaxia2016/NeuronalVariabilityStatsModels/blob/main/stats_model_fit_dataset1.ipynb).  In each matlab file, there are two variables: * spikes: a cell contains two arrays * The first element (#n1 neurons x 1280 ms x 3200 trials) contain number of spikes of each V1 neuron at each trial * The second element (#n2 neurons x 1280 ms x 3200 trials) contain number of spikes of each V2 neuron at each trial * stim: an (3200 trials x 1) array * an array indicates the orientation of drifting grating stimulus on each trial; in total there are 400 trials per orientation ordered randomly ###### dataset/dataset2: * V1_V2_contrast_150r002p017_combine_2_trials.pkl This folder contain 1 .pkl file. It contains a session of simultaneously recorded spike data from V1 and V2 (recorded by neuropixel probes) in response to drifting gratings with 8 orientations and 3 contrast levels. This dataset can be loaded using code from [https://github.com/tinaxia2016/NeuronalVariabilityStatsModels/blob/main/stats_model_fit_dataset2.ipynb](https://github.com/tinaxia2016/NeuronalVariabilityStatsModels/blob/main/stats_model_fit_dataset2.ipynb).  Here is an example of how to load the .pkl file in python: ``` import pickle with open('filename.pkl', 'rb') as f: data = pickle.load(f) ``` ###### stats\_model\_fitting\_res/stats\_model\_res: * dataset0_multidim_5_fold_cv_OTI_035.pkl * dataset1_multidim_5_fold_cv_OTI_035.pkl * dataset2_multidim_5_fold_cv_OTI_035.pkl * dataset3_multidim_5_fold_cv_OTI_035.pkl * dataset4_multidim_5_fold_cv_OTI_035.pkl * dataset2/ * 150_pytorch_neuropixel_all_contrasts_5_fold_cv_exclude_OTI_less_035_multidim_w_aff_con.pkl There are 5 .pkl files containing parameters of fitted statistical models fit to each of the 5 sessions from dataset 1 and 1 folder with 1 .pkl file for the 1 session from dataset 2. To access the object stored in the pickle files, please refer to code in [https://github.com/tinaxia2016/NeuronalVariabilityStatsModels](https://github.com/tinaxia2016/NeuronalVariabilityStatsModels) ###### joint\_stats\_model\_fitting\_res: * dataset0_joint_stats_models_5_fold_cv.pkl * dataset1_joint_stats_models_5_fold_cv.pkl * dataset2_joint_stats_models_5_fold_cv.pkl * dataset3_joint_stats_models_5_fold_cv.pkl * dataset4_joint_stats_models_5_fold_cv.pkl 5 .pkl files contain parameters of fitted joint statistical models to each of the 5 sessions from dataset 1. To access the object stored in the pickle files, please refer to code in [https://github.com/tinaxia2016/NeuronalVariabilityStatsModels](https://github.com/tinaxia2016/NeuronalVariabilityStatsModels) ## Code/Software You may use code from [https://github.com/tinaxia2016/NeuronalVariabilityStatsModels](https://github.com/tinaxia2016/NeuronalVariabilityStatsModels) to analyze and visualize the dataset.