Although pyramidal cells are the main excitatory neurons in the cerebral cortex, it has recently been reported that they can evoke inhibitory postsynaptic currents in neighboring pyramidal neurons. These inhibitory effects were proposed to be mediated by putative axo-axonic excitatory synapses between the axon terminals of pyramidal cells and perisomatic inhibitory axon terminals [Ren M, Yoshimura Y, Takada N, Horibe S, Komatsu Y (2007) Science 316:758-761]. However, the existence of this type of axo-axonic synapse was not found using serial section electron microscopy. Instead, we observed that inhibitory axon terminals synapsing on pyramidal cell bodies were frequently apposed by terminals that established excitatory synapses with neighbouring dendrites. We propose that a spillover of glutamate from these excitatory synapses can activate the adjacent inhibitory axo-somatic terminals.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.