In the neocortex, neuronal nitric oxide (NO) synthase (nNOS) is essentially expressed in two classes of GABAergic neurons: type I neurons displaying high levels of expression and type II neurons displaying weaker expression. Using immunocytochemistry in mice expressing GFP under the control of the glutamic acid decarboxylase 67k (GAD67) promoter, we studied the distribution of type I and type II neurons in the barrel cortex and their expression of parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP). We found that type I neurons were predominantly located in deeper layers and expressed SOM (91.5%) while type II neurons were concentrated in layer II/III and VI and expressed PV (17.7%), SOM (18.7%), and VIP (10.2%). We then characterized neurons expressing nNOS mRNA (n = 42 cells) ex vivo, using whole-cell recordings coupled to single-cell reverse transcription-PCR and biocytin labeling. Unsupervised cluster analysis of this sample disclosed four classes. One cluster (n = 7) corresponded to large, deep layer neurons, displaying a high expression of SOM (85.7%) and was thus very likely to correspond to type I neurons. The three other clusters were identified as putative type II cells and corresponded to neurogliaform-like interneurons (n = 19), deep layer neurons expressing PV or SOM (n = 9), and neurons expressing VIP (n = 7). Finally, we performed nNOS immunohistochemistry on mouse lines in which GFP labeling revealed the expression of two specific developmental genes (Lhx6 and 5-HT3A). We found that type I neurons expressed Lhx6 but never 5-HT3A, indicating that they originate in the medial ganglionic eminence (MGE). Type II neurons expressed Lhx6 (63%) and 5-HT3A (34.4%) supporting their derivation either from the MGE or from the caudal ganglionic eminence (CGE) and the entopeduncular and dorsal preoptic areas. Together, our results in the barrel cortex of mouse support the view that type I neurons form a specific class of SOM-expressing neurons while type II neurons comprise at least three classes.

Abstract. In the GRAAD project we are developing a knowledgebased system able of determine routes in a simulated urban environment and to generate natural language descriptions which are not distinguishable from those produced by human beings in similar conditions. In this paper, we present a new spatial model whose topology is based on the notion of object's influence areas. An influence area is a portion of space that people mentally build around spatial objects to take into account neighborhood. We use this notion to formally define the properties of neighborhood, orientation and distance in a qualitative way. We also introduce the notion of an object's perception area, an area gathering all the locations from which an object can be perceived. Based on these notions, we describe two modules of the GRAAD System that are able to find routes in a simulated urban environment and to generate route descriptions in natural language which are analog to those created by people.

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