Figure 6.

Data length dependence effect on monosynaptic connection inference. A: data length dependence effect on connectivity inference was evaluated with a subsampling method. Data length was measured in terms of geometric mean number of spikes, which was calculated by taking the square root of the product of total ventral posteromedial nucleus (VPm) and primary somatosensory cortex (S1) spikes. Random subsampling of the data set was performed in the unit of trials with 1,000 iterations for each condition. B: representative example pair of neurons from not-connected distribution in a rat. C: mean and standard deviation of peak height from cross correlogram were computed for each subsample of not-connected example. Blue line, short-data length condition (GM: 1,837 spikes); green line, long-data length condition (GM: 4,730 spikes). D: distribution of peak height after bootstrapping for 2 data lengths labeled in part I. E: probability of outcome for this example. CR, correct reject; FA, false alarm. F–I: similar to B–E but for connected example in a rat [blue (GM): 3,760 spikes, hit rate 87.7%, miss rate 12.3%; green (GM): 10,201 spikes, hit and miss rates 100%, 0%] (also see Supplemental Fig. S5; see https://doi.org/10.6084/m9.figshare.14393582.v1). J: bootstrap estimator of bias for each data length. Scatterplot of population data for connected pairs (n = 6 pairs, N = 3 rats). Solid line, exponential fit (R² = 0.76). K: variance of peak height at each data length for pairs shown in J. Solid line, 1st-order polynomial fit (R² = 0.62%). L: geometric mean for all connected pairs (median: 8,741 spikes, n = 6 pairs, N = 3 rats).