Extended Data Fig. 9: Activity and inference in the random balanced networks that are fully or sparsely connected.
From: Systematic errors in connectivity inferred from activity in strongly recurrent networks
a, Top: waterfall plots of neural fields of the full network at weak weights (when activity decays) and strong weights (when activity is chaotic), in response to a brief uniform feed-forward pulse. Bottom: corresponding inferences. (b-d) Inference on the sparse network. b, Left: true and inferred weights (using logistic regression) for the network with no noisy drive, at rRSB = 0.6. Some zero weights are inferred to be non-zero, while some nonzero weights are underestimated. Right: true and inferred weights (using only logistic regression, and augmented with CCG information) for the same condition, but when the network is noise-driven. c, Inference error (using the two methods) vs recurrent weight strength, and data volume, on the noise-driven network. d, Example CCG (top) and its time-derivative (bottom) of a connected neuron pair in the noise-driven network.
