This dataset describes the relationship between neural response variability and surround suppression. Typically, manipulations that elicit neural response suppression quench variability, an observation that has led some to suggest that these two phenomena may share a common origin. However, few studies have systematically examined the relationship between surround suppression and variability. Surround suppression is mediated by multiple circuits and mechanisms that depend on the size of the sensory stimulus and the cortical layer. Variability is also laminar-dependent. This dataset helps to understand how surround suppression affects neural response variability across layers of the visual cortex. The dataset contains electrophysiological laminar recordings that examine how neural response variability and the shared variability among neurons are modulated by visual stimulus size across the layers of macaque primary visual cortex (V1).

Dataset DOI: 10.5061/dryad.hqbzkh1x4

Description of the data and file structure

Data from: Size tuning of neural response variability in laminar circuits of macaque primary visual cortex

This is the data for the analyses published in the article "Size tuning of neural response variability in laminar circuits of macaque primary visual cortex." doi: 10.1101/2023.01.17.524397

Methods for data collection are described in the above publication.

The data in this repository was collected by performing linear array recordings of neural activity from the primary visual cortex of anesthetized macaque monkeys (macaca fascicularis). Great care was taken to ensure that the probe was inserted vertically into the cortex. At the beginning of each penetration, the receptive fields of the recorded units were mapped, their stimulus preferences determined, and the size tuning functions were measured with drifting sinusoidal luminance gratings.

Files and variables

The files in this submission come in two formats, .npy, .pkl, and .mat. To interact with the .npy files, Use NumPy package from SciPy, then load Python and use these commands:

import numpy as np
data = np.load("path/to/file.npy")
data

To interact with the .pkl files, use pickle that from the standard Python distribution and do, for example,

import pickle as pkl
with open('path/to/file.pkl,'rb') as f
     data = pkl.load(f)
data

To interact with the .mat files, download for example the open source software Octave, and do

data = load('/path/to/file.mat');
data

File: selectedData_MUA_lenient_400ms_macaque_July-2020.pkl

Description: This file contains the multi-unit spiking data of all recorded units without cleaning or filtering, organized into trials. Loading the data file returns a two-dimensional Python dictionary. The first dimension is the unit, and the second contrast. In this dataset, the contrast is always 100. To access the data, for example, for unit 0 do

unit_data = data[0][100.0]
unit_data['spkC_NoL'] # spike counts in each trial in an array with shape (trials, stimulus diameters)
unit_data['spkR_NoL']  # binned (1ms bins) spike counts an array with shape (trials, stimulus diameters, time), there's 400 ms of pre-stimulus baeline, followed by 500 ms when the visual stimulus was on the screen, and 100 ms post-stimulus baseline

# you can find info about each unit by doing, say,
unit_info = data[0]['info'] # unit info for unit 0
# unit_info is a dictionary with four variables, 'diam' this is an array of stimulus diameters (in degrees of visual angle) used in the recordings, 'layer', is the cortical layer from which the recording originated, 'animal' is the animal number, and 'penetr' is the penetration number

File: mean_PSTHs_SG-MK-MU-Dec-2021.pkl

Description: This is a Python dictionary containing spike counts of each unit computed in 100 ms sliding windows for supra-granular layer units. It is used as an intermediate file in the provided analysis codes.

File: mean_PSTHs_SG-MK-MU.pkl

Description: The same as above, but contains also information about the stimulus.

File: mean_PSTHs_G-MK-MU.pkl

Description: The same as above, but for granular layer units.

File: mean_PSTHs_G-MK-MU-Dec-2021.pkl

Description: The same as above, but for granular layer units.

File: mean_PSTHs_IG-MK-MU-Dec-2021.pkl

Description: The same as above, but for infra-granular layer units.

File: mean_PSTHs_IG-MK-MU.pkl

Description: The same as above, but for infra-granular layer units.

File: vari_PSTHs_G-MK-MU-Dec-2021.pkl

Description: This is a Python dictionary containing the variance of the spike counts of each unit computed in 100 ms sliding windows for granular layer units. It is used as an intermediate file in the provided analysis codes.

File: vari_PSTHs_IG-MK-MU-Dec-2021.pkl

Description: The same as above, but for infra-granular layer units.