Animals typically perceive natural odor cues in their olfactory environment as a complex mixture of chemically diverse components. In insects, the initial representation of an odor mixture occurs in the first olfactory center of the brain, the antennal lobe (AL). The contribution of single neurons to the processing of complex mixtures in insects, and in particular moths, is still largely unknown. Using a novel multicomponent stimulus system to equilibrate component and mixture concentrations according to vapor pressure, we performed intracellular recordings of projection and interneurons in an attempt to quantitatively characterize mixture representation and integration properties of single AL neurons in the moth. We found that the fine spatiotemporal representation of 2–7 component mixtures among single neurons in the AL revealed a highly combinatorial, non-linear process for coding host mixtures presumably shaped by the AL network: 82% of mixture responding projection neurons and local interneurons showed non-linear spike frequencies in response to a defined host odor mixture, exhibiting an array of interactions including suppression, hypoadditivity, and synergism. Our results indicate that odor mixtures are represented by each cell as a unique combinatorial representation, and there is no general rule by which the network computes the mixture in comparison to single components. On the single neuron level, we show that those differences manifest in a variety of parameters, including the spatial location, frequency, latency, and temporal pattern of the response kinetics.

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A method for isolating three-dimensional features of known height in the presence of noisy data is presented. The approach is founded upon observing the locations of a single light stripe in the image planes of two spatially separated cameras. Knowledge relating to the heights of sought features is used to define regions of interest in each image which are searched in order to isolate the light stripe. This approach is advantageous since spurious features that may result from random reflections or refractions in the region of interest of one image usually do not appear in the corresponding region of interest of the other image. It is shown that such a system is capable of robustly locating features such as very thin vertical dividers even in the presence of spurious or noisy image data that would normally cause conventional single camera light striping systems to fail. The discussion that follows summarizes the advantages of the methodology in relation to conventional passive steroscopic systems as well as light striped triangulation systems. Results that characterize the approach in noisy images are also provided.

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A method foc recognizing closed containers based on features extracted from their circular tops is presented. The approach developed consists of obtaining images from two spatially separated cameras that utilize both diffuse and specular light sources. The images thus obtained are used to segment target objects from the background and to extract representative features. The features utilized consist of container height as computed using stereopsis as well as the mean, variance and second central moments of the intensities of the segmented caps. The recognition procedure is based on a minimum distance Mahalanobis classifier which takes feature covariance into account. The discussion that follows details the algorithmic approach for the entire system including image acquisition, object segmentation, feature extraction and pattern classification. Result of test runs involving sets of several hundred training samples and untrained samples are presented.