960 resultados para Projection cortico-corticale
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Mode of access: Internet.
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Thesis (doctoral)--Universite de Louvain.
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The apposition compound eyes of gonodactyloid stomatopods are divided into a ventral and a dorsal hemisphere by six equatorial rows of enlarged ommatidia, the mid-band (MB). Whereas the hemispheres are specialized for spatial vision, the MB consists of four dorsal rows of ommatidia specialized for colour vision and two ventral rows specialized for polarization vision. The eight retinula cell axons (RCAs) from each ommatidium project retinotopically onto one corresponding lamina cartridge, so that the three retinal data streams (spatial, colour and polarization) remain anatomically separated. This study investigates whether the retinal specializations are reflected in differences in the RCA arrangement within the corresponding lamina cartridges. We have found that, in all three eye regions, the seven short visual fibres (svfs) formed by retinula cells 1-7 (R1-R7) terminate at two distinct lamina levels, geometrically separating the terminals of photoreceptors sensitive to either orthogonal e-vector directions or different wavelengths of light. This arrangement is required for the establishment of spectral and polarization opponency mechanisms. The long visual fibres (lvfs) of the eighth retinula cells (R8) pass through the lamina and project retinotopically to the distal medulla externa. Differences between the three eye regions exist in the packing of svf terminals and in the branching patterns of the lvfs within the lamina. We hypothesize that the R8 cells of MB rows 1-4 are incorporated into the colour vision system formed by R1-R7, whereas the R8 cells of MB rows 5 and 6 form a separate neural channel from R1 to R7 for polarization processing.
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In this paper we survey five streams of research that have made important contributions to population projection methodology over the last decade. These are: (i) the evaluation of population forecasts; (ii) probabilistic methods; (iii) experiments in the projection of migration; (iv) projecting dimensions additional to age, sex and region; and (v) the use of scenarios for 'what if?' analyses and understanding population dynamics. Key developments in these areas are discussed, and a number of opportunities for further research are identified. Copyright (c) 2005 John Wiley & Sons, Ltd.
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DCC (deleted in colorectal cancer)-the receptor of the netrin-1 neuronal guidance factor-is expressed and is active in the central nervous system (CNS) during development, but is down-regulated during maturation. The substantia nigra contains the highest level of netrin-1 mRNA in the adult rodent brain, and corresponding mRNA for DCC has also been detected in this region but has not been localized to any particular neuron type. In this study, an antibody raised against DCC was used to determine if the protein was expressed by adult dopamine neurons, and identify their distribution and projections. Significant DCC-immunoreactivity was detected in midbrain, where it was localized to ventrally displaced A9 dopamine neurons in the substantia nigra, and ventromedial A10 dopamine neurons predominantly situated in and around the interfascicular nucleus. Strong immunoreactivity was not detected in dopamine neurons found elsewhere, or in non-dopamine-containing neurons in the midbrain. Terminal fields selectively labeled with DCC antibody corresponded to known nigrostriatal projections to the dorsolateral striatal patches and dorsomedial shell of the accumbens, and were also detected in prefrontal cortex, septum, lateral habenular and ventral pallidum. The unique distribution of DCC-immunoreactivity in adult ventral midbrain dopamine neurons suggests that netrin-1/DCC signaling could function in plasticity and remodeling previously identified in dopamine projection pathways. In particular, a recent report that DCC is regulated through the ubiquitin-proteosome system via Siah/Sina proteins, is consistent with a potential involvement in genetic and sporadic forms of Parkinson's disease. (c) 2005 IBRO. Published by Elsevier Ltd. All rights reserved.
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It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 19-dimensional data sets.
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In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.
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It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.
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Layer 5 contains the major projection neurons of the neocortex and is composed of two major cell types: regular spiking (RS) cells, which have cortico-cortical projections, and intrinsic bursting cells (IB), which have subcortical projections. Little is known about the plasticity processes and specifically the molecular mechanisms by which these two cell classes develop and maintain their unique integrative properties. In this study, we find that RS and IB cells show fundementally different experience-dependent plasticity processes and integrate Hebbian and homeostatic components of plasticity differently. Both RS and IB cells showed TNFα-dependent homeostatic plasticity in response to sensory deprivation, but IB cells were capable of a much faster synaptic depression and homeostatic rebound than RS cells. Only IB cells showed input-specific potentiation that depended on CaMKII autophosphorylation. Our findings demonstrate that plasticity mechanisms are not uniform within the neocortex, even within a cortical layer, but are specialized within subcircuits.
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Whereas projection of self-attributes to ingroups is ubiquitous, projection of self-attributes to outgroups (outgroup projection) is an elusive phenomenon. Two experiments examined the moderating effect of perceived intergroup relationship on outgroup projection and explored underlying mechanisms. Perceived cooperation versus competition between ingroup and outgroup was manipulated using fictitious (Experiment 1) or natural groups (Experiment 2). In both experiments, participants judged the outgroup as more similar to the self in the cooperation condition than in the competition condition. This effect was independent of recategorization, perceived intergroup similarity, and ingroup-to-outgroup projection. These studies demonstrate the very existence of outgroup projection and extend previous work on moderators of projection from self to groups.
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Abnormal neuronal intermediate filament (IF) inclusions immunopositive for the type IV IF α-internexin have been identified as the pathological hallmark of neuronal intermediate filament inclusion disease (NIFID). We studied the topography of these inclusions in the frontal and temporal lobe in 68 areas from 10 cases of NIFID. In the cerebral cortex, CA sectors of the hippocampus, and dentate gyrus granule cell layer, the inclusions were distributed mainly in regularly distributed clusters, 50-800 μm in diameter. In seven cortical areas, there was a more complex pattern in which the clusters of inclusions were aggregated into larger superclusters. In 11 cortical areas, the size of the clusters approximated to those of the cells of origin of the cortico-cortical pathways but in the majority of the remaining areas, cluster size was smaller than 400 μm. The topography of the lesions suggests that there is degeneration of the cortico-cortical projections in NIFID with the formation of α-internexin-positive aggregates within vertical columns of cells. Initially, only a subset of cells within a vertical column develops inclusions but as the disease progresses, the whole of the column becomes affected. The corticostriate projection appears to have little effect on the cortical topography of the inclusions. © 2006 EFNS.