Dependence of contextual modulation in macaque V1 on interlaminar signal flow
Data files
Dec 17, 2025 version files 19.89 MB
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data_BO_session1.mat
2.49 MB
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data_BO_session2.mat
3.43 MB
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data_BO_session3.mat
1.51 MB
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data_BO_session4.mat
1.50 MB
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data_ccg_CRF.mat
5.95 MB
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data_ccg_nCRF.mat
5.01 MB
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README.md
4.93 KB
Abstract
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.
Dataset DOI: 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 Bown). 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.