Inferring neural circuit structure from datasets of heterogeneous tuning curves
Fig 3
Summary of the feedforward model fit to the M1 tuning curve dataset of Ref. [4].
(A) Data and model tuning curves at different stages of training; the plotted tuning curves are the projections of the 3D tuning curves along the preferred position (PP) vector which is obtained by a linear fit to the 3D tuning curve (see Ref. [4]). (B) Kolmogorov-Smirnov (KS) distance throughout training between the data and model distributions, shown in panels C–F, of four summary statistics characterizing the tuning curves evaluated on held-out data. Vertical dashed lines mark the epochs at which curves in panel A are sampled. (C–F) histograms of four tuning curve statistics (R2, complexity score, firing rates, and coding level) showing the data distribution (black), the initial model distribution (blue) and the final model distribution after fitting (red). Vertical lines show the mean value of each histogram.