The connectome — the connection matrix for the connectivity between almost 10 million neurons — is broken up by source and target hemisphere and by target region. That means, that a file might for example contain all ipsi-lateral connections into MOs of the right hemisphere.

Connectomes are stored as scipy.sparse.csc_matrix objects in .npz format.

To read in python:

from scipy import sparse

M = sparse.load_npz('MOs_right_ALL_INPUTS_ipsi.csc.npz')

Each such connection matrix will have a shape of N x M, where N is the total number of neurons in the model (almost 10 million) and M is the number of neurons in the target region (depends on that region). This means that the first axis denotes the presynaptic neuron and the second axis the postsynaptic neuron.

Each matrix file is accompanied by a file that specifies the global indices of the target neurons, i.e. the indices of the same neurons along the first axis. It contains a numpy.array of length M in .npy format. It can be used for example to find the connectivity originating from the same neurons in a different matrix and will be required to assemble a full N x N matrix.

To read in python:

import numpy

idx = numpy.load('MOs_right_ALL_INPUTS_indices.npy')