Fig. 4. aABC inference with a generative model based on the OU process in a branching network.

a, A schematic of a fully connected branching network with binary neurons. Branching parameter (m = 0.96) and number of neurons (k = 10⁴) define the probability of activity propagation (p). Each neuron receives an external input with probability h = 10⁻³. b, The shape of the sample autocorrelation of simulated activity in the branching network (brown) deviates from the ground truth (gray), but is accurately reproduced by the autocorrelation of synthetic data generated from a one-timescale OU process with the MAP estimate timescale from the aABC method. τ_MAP = 24.9. The data contained 100 trials of 500 time-steps. c, The aABC posterior distribution includes the ground-truth timescale. τ_{ground truth} = 24.5, τ_aABC = 24.9 ± 0.9 (mean ± std). Fitting parameters are provided in Supplementary Table 2