Online-only Table 1.

List of Matlab, Snakemake and Python scripts used for data processing and technical validation, with script names and descriptions.

Matlab data processing scripts
Task	Script name	Description
All except impedance data	align_ns6_nev.m	Temporal alignment of raw neuronal data across the NS6 files that are generated by the eight NSPs.
Resting state	generate_processed_resting_state.m	Extraction of MUAe and LFP data from the aligned NS6 files.
SNR	analyse_CheckSNR2.m	Extraction of MUAe data from raw NS6 files, and calculation of signal-to-noise ratio of visually evoked responses.
RF	analyse_RF_barsweep.m	Extraction of MUAe data from raw NS6 files, to identify the peak responses to sweeping bar stimuli.
	analyse_RF_barsweep_coordinates.m	Calculation of RF properties using MUAe data.
	combine_best_RF_sessions.m	Combination of RF data across two sessions, in which different types of bar stimuli were presented.
	plot_all_RFs_resting_state.m	Generates figures showing the RF centres across all the channels.
-	impedance_plotter.m	Compile impedance measurements across all NSPs.
Snakemake workflow scripts
Script name		Description
Snakefile		Main workflow file, executes all other scripts to generate the processed data and figures.
environment.yaml		Conda environment file. Contains full list of all Snakemake and Python dependencies. Can be used to recreate the Conda environment that was used to run the workflow.
Python data processing scripts
Task	Script name	Description
All	calculate_LFP.py	Calculates the LFP signal from a given array, annotates it with the corresponding metadata and saves it as one .nix file per array.
	calculate_MUA.py	Calculates the MUAe signal from a given array, annotates it with the corresponding metadata and saves it as a .nix file per array.
	utils.py	Several utility functions for the handling of metadata and annotation that are used by the other scripts.
	initialize_odml_from_xls.py	Creates a template .odml metadata file that contains the information of the experimental setup and specific subject.
	enrich_odml_IDs.py	Incorporates subject specific electrode IDs to the template .odml metadata file.
	enrich_odml_epochs.py	Includes subject and trial specific epoch/trial metadata such as the recording length, number of epochs (open or closed eyes in resting state sessions) or number of successful/failed trials (in SNR and RF tasks), as well as their timing and duration.
SNR & RF	generate_trial_csv.py	Generates a .csv file containing the trial start times, durations, conditions and whether they were successful. This .csv file is used by the enrichment scripts to incorporate this metadata into the .odml format.
	merge_csv.py	Utility function that merges a list of .csv files. Used to merge metadata.csv files from RF and SNR values from all different arrays into one.
SNR	calculate_SNR.py	Calculates the signal-to-noise ratio (SNR) from the MUAe.nix data of a checkerboard stimulus session and saves it as a .csv file, along the response amplitude, response onset and baseline specifications.
	arrayplot_SNR.py	Creates the plots in Figure 7a,b for each SNR session.
	arrayplot_response_timing.py	Creates the plots in Figure 7c,d for each SNR session.
	finalize_odml_SNR.py	Aggregates all metadata for a given SNR session and creates the final.odml.
RF	calculate_RF.py	Calculates the receptive field (RF) responses from the MUA.nix files of a corresponding session and generates a .csv file with RF centers, edges, signal-to-noise ratios and the goodness of the Gaussian fit.
	combine_RF.py	Combines the large and small bar receptive field (RF) responses into a single metadata file by selecting the best arrays from each session.
	finalize_odml_RF.py	Aggregates all metadata for a given RF session and creates the final .odml. The combined RF metadata are used for both large and small bar RF sessions.
Resting state	eyesig_conversion_and_epochs.py	Converts the eye signals from .mat to .nix and estimates when the eyes were open or closed from the pupil diameter. The estimated epochs are save as.csv files.
	eye_epochs_plot.py	Creates the plots in Fig. 6 for each resting state session.
	finalize_odml_RS.py	Aggregates the corresponding SNR and RF values to the metadata of a resting state session and creates the final .odml. SNR values from the same day and combined RF values are used.
Cross-talk removal	highpass_ns6.py	Filters the raw data for extraction of threshold crossings. This file is found in the signal processing folder, but only used for cross-talk removal.
	get_thr_crossings_mpi.py	Extracts threshold crossings from filtered raw signal.
	count_synchrofacts.py	Estimates the presence of synchrofacts by creating complexity histograms from the threshold crossings and surrogates.
	systematic_removal_of_electrodes.py	Removal of electrodes with highest above-chance synchronous events and plotting of the synchrofact process.
	utils.py	Utility functions for cross-talk removal.