Extended Data Fig. 7: Circuit inference using neural CCGs.
From: Systematic errors in connectivity inferred from activity in strongly recurrent networks
(a-d) Inferring the strong weight (r = 0.025) ring circuit using short-lag peaks in neural CCGs. a, Pearson’s cross-correlation of a connected and an unconnected neuron pair. b, Left: matrix of absolute lags of the CCG peaks. This partly reveals the circuit: directly connected neurons exhibit short lags. Right: binary matrix connecting neuron pairs with short lags. c, Top: CCG-based weight matrix: weight is set to the peak cross-correlation if the neurons are connected (lag < τ), zero otherwise. Bottom: avg. weight profile. GLM fares better. d, Combining CCG and GLM. Top: matrix of GLM-inferred weights if neuron pairs have short CCG lag, zero otherwise. Bottom: avg. weight profile. Relative to pure GLM (Δ = 0.23), this method (Δ = 0.32) removes some biases but introduces others. (e-j) Spike CCGs from the sparse, non-symmetric, strong weight (rRSA = 0.1) random network. e, Unconnected pair. CCG has no sharp features around 0, indicating no direct connection. f, Unconnected pair. Broad symmetric peak, indicating multiple indirect influences through mutual connections, is discounted. g, Connected pair. Sharp asymmetric dip at 0 reveals direct (inhibitory) connection. h, Unconnected pair. CCG is indistinguishable from the previous, and passes the criterion. i, Connected pair, but no CCG features. j, Connected pair, but broad symmetric peak is discounted.
