Stochastic activity in low-rank recurrent neural networks
Fig 6
Stochastic activity in a rank-one excitatory-inhibitory circuit.
A. E-I circuit with high-dimensional inputs. B. Variance explained by PC1 (top) and PC2 (bottom) as a function of the overall recurrent connectivity strength w and the relative dominance of inhibition g. In B–C–D, the black solid line separates the regions for which the non-zero eigenvalue λ is larger or smaller than one. The black dotted line separates the regions for which the non-zero eigenvalue λ is larger or smaller than zero. Note the different color scales in the top and bottom plots. C. Overlap between PC1 and the sum (top) and diff (bottom) directions. D. Non-zero eigenvalue of the synaptic connectivity matrix −
. E. E-I circuit with one-dimensional inputs. F. Variance explained by PC1 (top) and PC2 (bottom) as a function of the overall recurrent connectivity strength w and the direction of the input vector u. The input direction is parametrized by an angle θ (see Methods 8), so that
(resp.
) correspond to inputs entering only E (resp. I), while
(resp.
) corresponds to inputs aligned with the sum (resp. diff) direction. G. Variance explained by PC1 (top) and PC2 (bottom) as a function of the relative dominance of inhibition g and the direction of the input vector u. H. Overlap between PC1 and the sum (top) and diff (bottom) direction.