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Photoreceptor noise sets an absolute limit for the accuracy of colour discrimination. We compared colour thresholds in the honeybee (Apis mellifera) with this limit. Bees were trained to discriminate an achromatic stimulus from monochromatic lights of various wavelengths as a function of their intensity. Signal-to-noise ratios were measured by intracellular recordings in the three spectral types of photoreceptor cells. To model thresholds we assumed that discrimination was mediated by opponent mechanisms whose performance was limited by receptor noise. Most of the behavioural thresholds were close to those predicted from receptor signal-to-noise ratios, suggesting that colour discrimination in honeybees is affected by photoreceptor noise. Some of the thresholds were lower than this theoretical limit, which indicates summation of photoreceptor cell signals.

The anatomical substrates of neural nets are usually composed from reconstructions of neurons that were stained in different preparations. Realistic models of the structural relationships between neurons require a common framework. Here we present 3-D reconstructions of single projection neurons (PN) connecting the antennal lobe (AL) with the mushroom body (MB) and lateral horn, groups of intrinsic mushroom body neurons (type 5 Kenyon cells), and a single mushroom body extrinsic neuron (PE1), aiming to compose components of the olfactory pathway in the honeybee. To do so, we constructed a digital standard atlas of the bee brain. The standard atlas was created as an average-shape atlas of 22 neuropils, calculated from 20 individual immunostained whole-mount bee brains. After correction for global size and positioning differences by repeatedly applying an intensity-based nonrigid registration algorithm, a sequence of average label images was created. The results were qualitatively evaluated by generating average gray-value images corresponding to the average label images and judging the level of detail within the labeled regions. We found that the first affine registration step in the sequence results in a blurred image because of considerable local shape differences. However, already the first nonrigid iteration in the sequence corrected for most of the shape differences among individuals, resulting in images rich in internal detail. A second iteration improved on that somewhat and was selected as the standard. Registering neurons from different preparations into the standard atlas reveals 1) that the m-ACT neuron occupies the entire glomerulus (cortex and core) and overlaps with a local interneuron in the cortical layer; 2) that, in the MB calyces and the lateral horn of the protocerebral lobe, the axon terminals of two identified m-ACT neurons arborize in separate but close areas of the neuropil; and 3) that MB-intrinsic clawed Kenyon cells (type 5), with somata outside the calycal cups, project to the peduncle and lobe output system of the MB and contact (proximate) the dendritic tree of the PE1 neuron at the base of the vertical lobe. Thus the standard atlas and the procedures applied for registration serve the function of creating realistic neuroanatomical models of parts of a neural net. The Honeybee Standard Brain is accessible at www.neurobiologie.fu-berlin.de/beebrain.

By using harmonic radar, we report the complete flight paths of displaced bees. Test bees forage at a feeder or are recruited by a waggle dance indicating the feeder. The flights are recorded after the bees are captured when leaving the hive or the feeder and are released at an unexpected release site. A sequence of behavioral routines become apparent: (i ) initial straight flights in which they fly the course that they were on when captured (foraging bees) or that they learned during dance communication (recruited bees); (ii ) slow search flights with frequent changes of direction in which they attempt to ''get their bearings''; and (iii ) straight and rapid flights directed either to the hive or first to the feeding station and then to the hive. These straight homing flights start at locations all around the hive and at distances far out of the visual catchment area around the hive or the feeding station. Two essential criteria of a map-like spatial memory are met by these results: bees can set course at any arbitrary location in their familiar area, and they can choose between at least two goals. This finding suggests a rich, map-like organization of spatial memory in navigating honey bees.dance ͉ communication ͉ localization in navigation ͉ vector orientation ͉ vector map

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The antennal lobe of the moth brain is the primary olfactory center processing information about pheromones and plant odors. We present here a digital atlas of the glomerular antennal lobe structures in the male of Helicoverpa assulta and the male and female of Heliothis virescens, based on synaptic antibody staining combined with confocal microscopy. The numbers of the glomeruli in the three specimens were similar, 65, 66, and 62, respectively. Whereas the male antennal lobe has a macroglomerular complex consisting of three and four units in the two species, the female lobe has two enlarged glomeruli at a corresponding position, near the entrance of the antennal nerve. Another large glomerulus, showing homology in the three specimens, is ventrally located. The small size of the heliothine moths is advantageous for confocal microscopy because the entire brain can be visualized as a single image stack. The maps are freely accessible on the internet, and the digital form of the data allows each atlas to be rotated and sectioned at any angle, providing for the identification of glomeruli in different preparations.

There are many ways to segment an image, that is, to assign a semantic label to each of its pixels or voxels. Different segmentation techniques use different types of image information, prior knowledge about the problem at hand, and internal constraints of the segmented geometry. Which method is the most suitable in any given case depends on the image data, the objects imaged, and the type of desired output information.Purely intensity-based classification methods [29,76,81] work locally, typically one voxel at a time, by clustering the space of voxel values (i.e., image intensities). The clusters are often determined by an unsupervised learning method, for example, k-means clustering, or derived from example segmentations [43].Each cluster is identified with a label, and each voxel is assigned the label of the cluster corresponding to its value. This assignment is independent of the voxel's spatial location. Clustering methods obviously require that the label for each voxel is determined by its value. Extensions of clustering methods that avoid overlapping clusters work on vector-valued data, where each voxel carries a vector of intensity values. Such data is routinely generated by multispectral

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