Fig 3. AIS_d covaries with rheobase.

Comparisons were performed using a Bayesian analytical approach to calculate a posterior probability distribution. Plots are based on a generative model conditioned on previous reports and the current data set. Each grey line represents a single trial from 4,000 generative samples and each black dot is an observed data point (n = 16). From the 4,000 samples we provide a 95% high density interval (HDI). The median slope from the generative sample is represented as a blue line (β₁). From the slopes and generative model, we compute an R² equivalent, and this is presented with an HDI and a median. (A) Rheobase: AIS distance. As rheobase increases the AIS distance increased from the motoneuron soma. Model slope: 95% HDI 0.463–1.21, β₁ = 0.846; R²: 95% HDI 0.364–0.732, median R² 0.631. (B) Close similarity in rheobase and input conductance between two samples of MG motoneurons. A hierarchical Bayesian model was constructed for 44 MG motoneurons (white filled circles, grey lines) pooled from a larger MG motoneuron database produced by the Cope lab and for 10 MG Neurobiotin filled motoneurons (black circles, blue lines) pooled from the 16 cells presented in Fig 3. Models were conditioned on previous reports and datasets from this study [44]. The positive correlation between rheobase and conductance is representative of prior reports and our small sample of 10 motoneurons fell within the expected range produced from our larger dataset. 44 motoneurons—Model slope: 95% HDI 15.5–25.0, β₁ = 20.2; R²: 95% HDI 0.522–0.728, median R² 0.644. Neurobiotin filled motoneurons—Model slope: 95% HDI 10.9–42.2, β₁ = 26.0; R²: 95% HDI 0.239–0.743, median R² 0.648.