David C. West

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

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 Ca2+ 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.

Neocortical local synaptic circuitry revealed with dual intracellular recordings and biocytin-filling

Dual intracellular recordings in neocortex reveal that different classes of synapses each display their own unique properties, some dependent on the receptors and active and passive properties displayed postsynaptically, some on the time- and pattern-dependent characteristics of transmitter release.