DOI: 10.5281/zenodo.10812497
title: Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"

description: This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: 10.1101/2024.03.15.585196). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (Markram et al., 2015), refered to as "BBP" data in the article. Specificaly, following data items are included:

Simulation data: simulation.xz

"Reliability" protocol:

Separate folders BlobStimReliability_O1v5-SONATA_<circuit-name> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:



working_dir/connectome.h5: Connectivity matrix in ConnectivityMatrix format, which can be loaded using ConnectomeUtilities.

working_dir/raw_spikes_exc_<sim>.npy: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.

working_dir/stim_stream.npy: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.

working_dir/time_windows.npy: Time windows in ms in numpy .npy format, corresponding to the stimulus train.

working_dir/processed_data_store.h5: Data store in HDF5 format with preprocessed spike signals (e.g., required for Gaussian kernel reliability computations), which contains...



spike_signals_exc: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.

sigma: Sigma in ms of Gaussian kernel used for smoothing.

gids: List of excitatory neuron GIDs.

t_bins: List of time bins in ms.

firing_rates: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.




"Classification" protocol:

Single folder Toposample_O1v5-SONATA with simulation data using the baseline connectome, stored in a format compatible with the TriDy (Conceição et al., 2022) and TopoSampling (Reimann et al., 2022) pipelines, containing:



toposample_input/connectivity.npz: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using scipy.sparse.load_npz.

toposample_input/neuron_info.pickle: Pandas dataframe in pickle format, which can be loaded using pandas.read_pickle, containing additional information about each neuron.

toposample_input/raw_spikes_exc.npy: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).

toposample_input/stim_stream.npy: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.

toposample_input/time_windows.npy: Time windows in ms in numpy .npy format, corresponding to the stimulus train.


 

Classification data: classification.xz

Selected neighborhoods and classification results for the "PCA" method (TopoSampling pipeline) as well as the "network_based" method using active subnetworks (TriDy pipeline, using TriDy-tools wrapper), stored as:

"PCA" method:



PCA/community_database_PCA.pkl: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).

PCA/classification_results_PCA.pkl: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).


"network_based" method:



network_based/selections_reliability.pkl: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the double selection procedure (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).

network_based/partition_reliability.npy: Partition indices in numpy .npy format required to launch the pipeline using TriDy-tools, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in selections_reliability.pkl (rows).

network_based/results/...: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in selections_reliability.pkl (rows).


 



Funding

Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).
description: Uncompress .xz files: tar -xf <filename>.xz
description: Table with labels of the baseline and manipulated connectomes as used in the article (cf. Table 2 and Figure S8), and corresponding circuit names as in BlobStimReliability_O1v5-SONATA_<circuit-name>:




Label
<circuit-name>


Baseline
Baseline


Order 1
ConnRewired_mc2EE_Order1


Order 2
ConnRewired_mc2EE_Order2


Order 3
ConnRewired_mc2EE_Order3


Order 4
ConnRewired_mc2EE_Order4


Order 5
ConnRewired_mc2EE_Order5


RC - 1
RecipRemoval_StructDim56


RC - 2
RecipRemoval_StructDim56_456


RC - 3
RecipRemoval_StructDim456


RC - 4
RecipRemoval_Unstruct-3


RC* - 1
RecipRemoval_Unstruct-0


RC* - 2
RecipRemoval_Unstruct-1


RC* - 3
RecipRemoval_Unstruct-2


RC + 1
ConnAdd_RecipStruct0x2


RC + 2
ConnAdd_RecipStruct0x3


RC + 3
ConnAdd_RecipStruct0x4


RC + 4
ConnAdd_RecipStruct0x5


RC + 5
ConnAdd_RecipStruct0x8


RC + 6
ConnAdd_RecipStruct0x16


RC* + 1
ConnAdd_Control0x2


RC* + 2
ConnAdd_Control0x3


RC* + 3
ConnAdd_Control0x4


RC* + 4
ConnAdd_Control0x5


RC* + 5
ConnAdd_Control0x8


RC* + 6
ConnAdd_Control0x16


100k
ConnRewireEnhanced100K


200k
ConnRewireEnhanced200K


300k
ConnRewireEnhanced300K


400k
ConnRewireEnhanced400K


500k
ConnRewireEnhanced500K


670k
ConnRewireEnhanced670K




 
description: All simulatable circuits in SONATA format (Dai et al., 2020) are available from the lead authors upon request.