The expression patterns of 13 GABAA receptor subunit encoding genes (alpha 1-alpha 6, beta 1-beta 3, gamma 1-gamma 3, delta) were determined in adult rat brain by in situ hybridization. Each mRNA displayed a unique distribution, ranging from ubiquitous (alpha 1 mRNA) to narrowly confined (alpha 6 mRNA was present only in cerebellar granule cells). Some neuronal populations coexpressed large numbers of subunit mRNAs, whereas in others only a few GABAA receptor-specific mRNAs were found. Neocortex, hippocampus, and caudate-putamen displayed complex expression patterns, and these areas probably contain a large diversity of GABAA receptors. In many areas, a consistent coexpression was observed for alpha 1 and beta 2 mRNAs, which often colocalized with gamma 2 mRNA. The alpha 1 beta 2 combination was abundant in olfactory bulb, globus pallidus, inferior colliculus, substantia nigra pars reticulata, globus pallidus, zona incerta, subthalamic nucleus, medial septum, and cerebellum. Colocalization was also apparent for the alpha 2 and beta 3 mRNAs, and these predominated in areas such as amygdala and hypothalamus. The alpha 3 mRNA occurred in layers V and VI of neocortex and in the reticular thalamic nucleus. In much of the forebrain, with the exception of hippocampal pyramidal cells, the alpha 4 and delta transcripts appeared to codistribute. In thalamic nuclei, the only abundant GABAA receptor mRNAs were those of alpha 1, alpha 4, beta 2, and delta. In the medial geniculate thalamic nucleus, alpha 1, alpha 4, beta 2, delta, and gamma 3 mRNAs were the principal GABAA receptor transcripts. The alpha 5 and beta 1 mRNAs generally colocalized and may encode predominantly hippocampal forms of the GABAA receptor. These anatomical observations support the hypothesis that alpha 1 beta 2 gamma 2 receptors are responsible for benzodiazepine I (BZ I) binding, whereas receptors containing alpha 2, alpha 3, and alpha 5 contribute to subtypes of the BZ II site. Based on significant mismatches between alpha 4/delta and gamma mRNAs, we suggest that in vivo, the alpha 4 subunit contributes to GABAA receptors that lack BZ modulation.

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.

Networks of GABAergic interneurons are of critical importance for the generation of gamma frequency oscillations in the brain. To examine the underlying synaptic mechanisms, we made paired recordings from ''basket cells'' (BCs) in different subfields of hippocampal slices, using transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of the parvalbumin promoter. Unitary inhibitory postsynaptic currents (IPSCs) showed large amplitude and fast time course with mean amplitudeweighted decay time constants of 2.5, 1.2, and 1.8 ms in the dentate gyrus, and the cornu ammonis area 3 (CA3) and 1 (CA1), respectively (33-34°C). The decay of unitary IPSCs at BC-BC synapses was significantly faster than that at BC-principal cell synapses, indicating target cell-specific differences in IPSC kinetics. In addition, electrical coupling was found in a subset of BC-BC pairs. To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20 -110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.G amma frequency oscillations are thought to be of key importance for higher brain functions, such as feature binding and temporal encoding of information (1-5). Experimental and theoretical evidence suggests that local networks of synaptically connected GABAergic interneurons are critically involved in the generation of these oscillations (6-19). First, perisomatic inhibitory interneurons (basket cells) fire action potentials at high frequency during gamma activity in vivo, with single spikes phase-locked to the oscillations of the field potential (6, 7). Second, pharmacologically isolated networks of inhibitory interneurons in vitro can oscillate at gamma frequency in response to metabotropic glutamate receptor activation (8). Finally, models of mutually connected interneurons generate coherent action potential activity in the gamma frequency range in the presence of a tonic excitatory drive (9-19).The mechanisms leading to the generation of coherent gamma oscillations in interneuron networks, however, have remained unclear. Although gamma frequency oscillations can be generated in interneuron network models, coherence is fragile against variation in amplitude and time course of the inhibitory postsynaptic conductance, against heterogeneity of the tonic excitatory drive, and against sparseness of connectivity (11-14). The mechanisms contributing to the control of network frequency are also poorly understood. It is thought that the time course of the inhibitory synaptic conductance change is a major factor (8-14), but the significance of other parameters remains undetermined. Some models suggest t...