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.

The spatio-temporal evolution of a turbulent swirling jet undergoing vortex breakdown has been investigated. Experiments suggest the existence of a self-excited global mode having a single dominant frequency. This oscillatory mode is shown to be absolutely unstable and leads to a rotating counter-winding helical structure that is located at the periphery of the recirculation zone. The resulting time-periodic 3D velocity field is predicted theoretically as being the most unstable mode determined by parabolized stability analysis employing the mean flow data from experiments. The 3D oscillatory flow is constructed from uncorrelated 2D snapshots of particle image velocimetry data, using proper orthogonal decomposition, a phase-averaging technique and an azimuthal symmetry associated with helical structures. Stability-derived modes and empirically derived modes correspond remarkably well, yielding prototypical coherent structures that dominate the investigated flow region. The proposed method of constructing 3D time-periodic velocity fields from uncorrelated 2D data is applicable to a large class of turbulent shear flows.

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a) Iconic representation. (b) Due to the shown separation surfaces, the topological skeleton of the vector field looks visually cluttered. (c) Visualization of the topological skeleton using saddle connectors.Figure 1: Topological representations of the benzene data set with 184 critical points. AbstractOne of the reasons that topological methods have a limited popularity for the visualization of complex 3D flow fields is the fact that such topological structures contain a number of separating stream surfaces. Since these stream surfaces tend to hide each other as well as other topological features, for complex 3D topologies the visualizations become cluttered and hardly interpretable. This paper proposes to use particular stream lines called saddle connectors instead of separating stream surfaces and to depict single surfaces only on user demand. We discuss properties and computational issues of saddle connectors and apply these methods to complex flow data. We show that the use of saddle connectors makes topological skeletons available as a valuable visualization tool even for topologically complex 3D flow data.

We describe a novel method for continuously transforming two triangulated models of arbitrary topology into each other. Equal global topology for both objects is assumed. However, extensions for genus changes during metamorphosis are provided. The proposed method addresses the major challenge in 3D metamorphosis, namely, specifying the morphing process intuitively with minimal user interaction and sufficient detail. Corresponding regions and point features are interactively identified. These regions are parametrized automatically and consistently, providing a basis for smooth interpolation. Suitable 3D interaction techniques offer a simple and intuitive control over the whole morphing process.

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