Extended Data Fig. 4: Dimensionality of the activity and rank of connectivity in the data-constrained RNNs, related to Fig. 5.
From: Prediction of neural activity in connectome-constrained recurrent networks
A Neural activity traces (centered) used for training the student networks for the three different data constrained RNNs: the premotor network in the Drosophila larva, the central complex in the adult Drosophila, and the oculomotor integrator in larval zebrafish. Different trials/conditions have been concatenated. B Left: First eigenvalues of the covariance spectrum of the datasets. Right: Participation ratio of the activity covariance. The dimensionality of neural activity is higher in the premotor system, then the CX and then the premotor network, indicated by how fast the eigenvalues decay. C Left: Singular values of the connectivity matrix. Right: Estimated rank of the connectivity matrix J, calculated using the participation ratio of the distribution of singular values of J. Given the sparsity and heterogeneity in connectomes, the rank of the connectivity is high.
