We studied thalamocortical afferent (TCA) growth into somatosensory cortex as the whisker barrels emerge in postnatal mice. Ingrowing fibers from the ventrobasal (VB) thalamus were selectively labeled by two means. Under direct vision, individual axons and populations of axons were labeled in vitro with HRP, or in fixed tissue with Dil (1,1'-dioctodecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate), in pieces of brain containing both the source nucleus in the thalamus and its cortical target. Many simple thalamocortical afferents are already within the upper cortical plate at birth [postnatal day one (PND1)]. Initially, TCAs from each point in the thalamus distribute in the cortex as two-dimensional "Gaussians," which overlap laterally to constitute a uniform projection pattern. The projection is topographic, because adjacent focal injections within VB label adjacent cortical loci. Subsequent development of barreloids (thalamic representations of the whiskers) partitions the TCA projection into a set of whisker-related Gaussians, centered on cortical targets whose collective topography reflects that of the source pattern. After barreloids form on about PND3, but before barrels appear in cytoarchitecture on about PND5, the overlapping TCAs segregate into dense terminal clusters in layer IV, around which barrels later mature. Time series of single fibers traced with camera lucida explain this transformation that is so noticeable at the population level. As early as PND1, individual TCAs emit multiple ascending collaterals on their horizontal run through white matter and oblique ascent into upper cortex. Subsequently, by PND4, and proceeding at least through PND7, there is accelerated terminal arborization of selected appropriate collateral branches and pruning back of other inappropriate ones. The selection mechanism appears to result from within-group reinforcement events that are stronger for branches toward the center of each whisker-related Gaussian distribution.

Throughout nature, elegant biophotonic structures have evolved into sophisticated arrangements of pigments and structural reflectors that manipulate light in the skin, cuticles, feathers and fur of animals. Not many spherical biophotonic structures are known and those described are often angle dependent or spectrally tuned. White light scattering by the flexible skin of cuttlefish (Sepia officinalis) is examined and how the unique structure and composition of leucophore cells serve as physiologically passive reflectors approximating the optical properties of a broadband Lambertian surface is investigated. Leucophores are cells that contain thousands of spherical microparticles called leucosomes that consist of sulfated glycoproteins or proteoglycans and reflectin. A leucophore containing ≈12 000 leucosome microspheres is characterized three‐dimensionally by electron microscopy and the average refractive index of individual leucosomes is measured by holographic microscopy to be 1.51 ± 0.02. Modeling of the ultrastructural data and spectral measurements with Lorenz‐Mie theory and Monte Carlo simulations suggest that leucophore whiteness is produced by incoherent scattering based upon a randomly ordered system. These soft, compliant, glycosylated proteinacious spheres may provide a template for bio‐inspired approaches to efficient light scattering in materials science and optical engineering.

Cuttlefish, Sepia officinalis, possess neurally controlled, pigmented chromatophore organs that allow rapid changes in skin patterning and coloration in response to visual cues. This process of adaptive coloration is enabled by the 500% change in chromatophore surface area during actuation. We report two adaptations that help to explain how colour intensity is maintained in a fully expanded chromatophore when the pigment granules are distributed maximally: (i) pigment layers as thin as three granules that maintain optical effectiveness and (ii) the presence of high-refractive-index proteins-reflectin and crystallin-in granules. The latter discovery, combined with our finding that isolated chromatophore pigment granules fluoresce between 650 and 720 nm, refutes the prevailing hypothesis that cephalopod chromatophores are exclusively pigmentary organs composed solely of ommochromes. Perturbations to granular architecture alter optical properties, illustrating a role for nanostructure in the agile, optical responses of chromatophores. Our results suggest that cephalopod chromatophore pigment granules are more complex than homogeneous clusters of chromogenic pigments. They are luminescent protein nanostructures that facilitate the rapid and sophisticated changes exhibited in dermal pigmentation.

Chromatophore organs in cephalopod skin are known to produce ultra-fast changes in appearance for camouflage and communication. Light-scattering pigment granules within chromatocytes have been presumed to be the sole source of coloration in these complex organs. We report the discovery of structural coloration emanating in precise register with expanded pigmented chromatocytes. Concurrently, using an annotated squid chromatophore proteome together with microscopy, we identify a likely biochemical component of this reflective coloration as reflectin proteins distributed in sheath cells that envelop each chromatocyte. Additionally, within the chromatocytes, where the pigment resides in nanostructured granules, we find the lens protein Ω- crystallin interfacing tightly with pigment molecules. These findings offer fresh perspectives on the intricate biophotonic interplay between pigmentary and structural coloration elements tightly co-located within the same dynamic flexible organ - a feature that may help inspire the development of new classes of engineered materials that change color and pattern.

It is generally assumed that the variability of neuronal morphology has an important e¡ect on both the connectivity and the activity of the nervous system, but this e¡ect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure^function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of threedimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing ¢les, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This`Cartesian' description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise`blueprint' of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of`fundamental', measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian ¢le (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and ampli¢cation can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data ampli¢cation is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L-NEURON and ARBORVITAE, to investigate systematically the potential of several di¡erent algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of d...