DOI: 10.5061/dryad.7m0cfxq94
title: Dependence of contextual modulation in macaque V1 on interlaminar signal flow

description: In visual cortex, neural correlates of subjective perception can be
 generated by modulation of activity from beyond the classical receptive
 field (CRF). In macaque V1, activity generated by nonclassical receptive
 field (nCRF) stimulation involves different intracortical circuitry than
 activity generated by CRF stimulation, suggesting that interactions
 between neurons across V1 layers differ under CRF and nCRF stimulus
 conditions. Using Neuropixels probes, we measured border ownership
 modulation within large, local populations of V1 neurons. We found that
 neurons in single columns preferred the same side of objects located
 outside of the CRF. In addition, we found that cross-correlations between
 pairs of neurons situated across feedback/horizontal and input layers
 differed between CRF and nCRF stimulation. Furthermore, independent of the
 comparison with CRF stimulation, we observed that the magnitude of border
 ownership modulation increased with the proportion of information flow
 from feedback/horizontal layers to input layers. These results demonstrate
 that the flow of signals between layers covaries with the degree to which
 neurons integrate information from beyond the CRF.
description: # Dependence of contextual modulation in macaque V1 on interlaminar signal
 flow Dataset DOI:
 [10.5061/dryad.7m0cfxq94](https://doi.org/10.5061/dryad.7m0cfxq94) ##
 Description of the data and file structure This dataset contains neuron
 response of 4 Neuropixels recording sessions from Macaque V1, under
 nonclassical receptive field stimulation, i.e. border-ownership (BO, or
 B~own~). Sessions 1-2 were collected from monkey 1; sessions 3-4 were from
 monkey 2.  ### Files and variables #### File: data_BO_session1.mat,
 data_BO_session2.mat, data_BO_session3.mat, data_BO_session4.mat
 **Description:** The data for each session is formatted as a MATLAB
 structure named "data_BO". ##### Variables *
 "spiketrain":array of size 'N_trials' x 'N_time
 bins' x 'N_neurons'. Spikes were binned with 1ms
 resolution, from 300ms before stimulus onset to 500ms after stimulus
 onset. * "trial_condition_ID": array of size
 'N_trials' x 4 'stimulus factors' (object side, local
 contrast, orientation, size). * "cluster_depth": array of size
 "N_neurons". The depth of each neuron in microns. 0 is the
 border between layer 4C and layers 5/6, with positive values indicating
 more superficial. * "cluster_layerID": array of size
 'N_neurons' indicating the laminar compartment which individual
 neuron resides in. 1-5 corresponds to layers 5/6, layer 4C, layers 4A/B,
 layers 2/3, and white matter, respectively. * "p_anova": array
 of size 'N_neurons' x 3 'factors'. The statistical
 significance of object side, local contrast, and their interaction, on the
 mean spike counts of each stimulus presentation. * "BO_index":
 array of 'N_neurons', border ownership modulation index for each
 neuron. * "LC_index": array of 'N_neurons', local
 contrast modulation index for each neuron. #### File: data_ccg_nCRF.mat
 **Description:** Significant CCG data from the 4 key laminar combinations
 (4C-2/3, 4A/B-2/3, 4C-5/6, 4A/B-5/6) under border ownership stimulation,
 for all sessions formatted as a MATLAB structure named
 "ccg_BO_sig". ##### Variables * "id_session": array of
 'N_neuronal pairs' indicating the recording session for each
 pair. * "first_ref_neuron_layer": array of 'N_neuronal
 pairs'. For each neuronal pair, Layer ID for the first neuron, or
 reference neuron in the CCG function. ID 2, 3 corresponds to layer 4C,
 layers 4A/B respectively. * "second_tgt_neuron_layer": array of
 'N_neuronal pairs'. For each neuronal pair, Layer ID for the
 second neuron, or target neuron in the CCG function. ID 1, 4 corresponds
 to layers 5/6, layers 2/3 respectively. * "CCG": array of
 'N_neuronal pairs x N_time points'. Raw CCG data (normalized and
 jitter-corrected), calculated from -100ms to 100ms time lag between two
 spikes trains, at a 1ms step. * "CCG_smoothed": array of
 'N_neuronal pairs x N_time points'. Smoothed CCG data
 (normalized and jitter-corrected) from the raw. * "PeakLag":
 array of 'N_neuronal pairs'. The time lag of peak occurrence for
 each smoothed CCG, in ms. * "Peak": array of 'N_neuronal
 pairs'. The max value for each smoothed CCG. * "Asymmetry":
 array of 'N_neuronal pairs'. Subtracting the sum of smoothed CCG
 values during the [-13, 0] ms time window from the sum of smoothed CCG
 values during the [0, 13] ms. * "MI_BO": array of
 'N_neuronal pairs'. The geometric mean of the border ownership
 index for each neuronal pair. * "MI_LC": array of
 'N_neuronal pairs'. The geometric mean of the local contrast
 index for each neuronal pair. #### File: data_ccg_CRF.mat
 **Description:** Significant CCG data from the 4 key laminar combinations
 (4C-2/3, 4A/B-2/3, 4C-5/6, 4A/B-5/6) under CRF (grating) stimulation, for
 all sessions formatted as a MATLAB structure named
 "ccg_Ori_sig". ##### Variables * "id_session": array
 of 'N_neuronal pairs' indicating the recording session for each
 pair. * "first_ref_neuron_layer": array of 'N_neuronal
 pairs'. For each neuronal pair, Layer ID for the first neuron, or
 reference neuron in the CCG function. ID 2, 3 corresponds to layer 4C,
 layers 4A/B respectively. * "second_tgt_neuron_layer": array of
 'N_neuronal pairs'. For each neuronal pair, Layer ID for the
 second neuron, or target neuron in the CCG function. ID 1, 4 corresponds
 to layers 5/6, layers 2/3 respectively. * "CCG": array of
 'N_neuronal pairs x N_time points'. Raw CCG data (normalized and
 jitter-corrected), calculated from -100ms to 100ms time lag between two
 spikes trains, at a 1ms step. * "CCG_smoothed": array of
 'N_neuronal pairs x N_time points'. Smoothed CCG data
 (normalized and jitter-corrected) from the raw. * "PeakLag":
 array of 'N_neuronal pairs'. The time lag of peak occurrence for
 each smoothed CCG, in ms. * "Peak": array of 'N_neuronal
 pairs'. The max value for each smoothed CCG. * "Asymmetry":
 array of 'N_neuronal pairs'. Subtracting the sum of smoothed CCG
 values during the [-13, 0] ms time window from the sum of smoothed CCG
 values during the [0, 13] ms. ## Software MATLAB R2023a.