Binomial parameters differ across neocortical layers and with different classes of connections in adult rat and cat neocortex.

Binomial model-based analysis compared excitatory connections involving different classes of neurons in different neocortical layers. Single-sweep excitatory postsynaptic potentials (EPSPs) from dual intracellular recordings in adult cat and rat slices were measured. For data subsets corresponding to first EPSPs exhibiting different degrees of posttetanic potentiation and second, third etc. EPSPs in trains at different interspike intervals, coefficient of variation (CV), transmission failure rates (F), variance (V), and V/M were plotted against mean EPSP amplitude (M). Curves derived from binomial models in which subsets varied only in p (release probability) were fit and parameters q (quantal amplitude), and n (number of release sites) were estimated. Estimates for q and n were similar for control subsets and subsets recorded during Ca(2+) channel blockade, only p varied. Estimates from the four methods were powerfully correlated, but when CV, F, V, and V/M were plotted against M, different types of connections occupied different regions of parameter space. Comparisons of linear fits to V/M against M plots and of parameter estimates indicated that these differences were significant. Connections between pyramids in different layers and inputs to different cell classes in the same layer differed markedly. Monte Carlo simulations of more complex models subjected to simple binomial model-based analysis confirmed the significance of these differences. Binomial models, either simple, in which p and q are identical at all terminals involved, or more complex, in which they differ, adequately describe many neocortical connections, but each class uses different combinations of n, mean p, and mean q.