994 resultados para neural differentiation


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In this work we emphasize why market coverage should be considered endogenous for a correct analysis of entry deterrence in vertical differentiation models and discuss the implications of this endogeneity for that analysis. We consider contexts without quality costs and also contexts with convex fixed quality costs.

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New methods of surface modification of transparent silicone substrate were developed, and a new set of cell culture devices that provide homogeneous substrate strain was designed. Using the new device, effects of cyclic substrate strain on bone marrow mesenchymal stem cells(MSCs) were studied. It was found that cyclic strain influenced proliferation and differentiation of bone marrow MSCs in different ways.

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This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

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We used multipotent stem cells (MSCs) derived from the young rat subventricular zone (SVZ) to study the effects of glutamate in oligodendrocyte maturation. Glutamate stimulated oligodendrocyte differentiation from SVZ-derived MSCs through the activation of specific N-methyl-D-aspartate (NMDA) receptor subunits. The effect of glutamate and NMDA on oligodendrocyte differentiation was evident in both the number of newly generated oligodendrocytes and their morphology. In addition, the levels of NMDAR1 and NMDAR2A protein increased during differentiation, whereas NMDAR2B and NMDAR3 protein levels decreased, suggesting differential expression of NMDA receptor subunits during maturation. Microfluorimetry showed that the activation of NMDA receptors during oligodendrocyte differentiation elevated cytosolic calcium levels and promoted myelination in cocultures with neurons. Moreover, we observed that stimulation of MSCs by NMDA receptors induced the generation of reactive oxygen species (ROS), which were negatively modulated by the NADPH inhibitor apocynin, and that the levels of ROS correlated with the degree of differentiation. Taken together, these findings suggest that ROS generated by NADPH oxidase by the activation of NMDA receptors promotes the maturation of oligodendrocytes and favors myelination

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Neurons in the songbird forebrain nucleus HVc are highly sensitive to auditory temporal context and have some of the most complex auditory tuning properties yet discovered. HVc is crucial for learning, perceiving, and producing song, thus it is important to understand the neural circuitry and mechanisms that give rise to these remarkable auditory response properties. This thesis investigates these issues experimentally and computationally.

Extracellular studies reported here compare the auditory context sensitivity of neurons in HV c with neurons in the afferent areas of field L. These demonstrate that there is a substantial increase in the auditory temporal context sensitivity from the areas of field L to HVc. Whole-cell recordings of HVc neurons from acute brain slices are described which show that excitatory synaptic transmission between HVc neurons involve the release of glutamate and the activation of both AMPA/kainate and NMDA-type glutamate receptors. Additionally, widespread inhibitory interactions exist between HVc neurons that are mediated by postsynaptic GABA_A receptors. Intracellular recordings of HVc auditory neurons in vivo provides evidence that HV c neurons encode information about temporal structure using a variety of cellular and synaptic mechanisms including syllable-specific inhibition, excitatory post-synaptic potentials with a range of different time courses, and burst-firing, and song-specific hyperpolarization.

The final part of this thesis presents two computational approaches for representing and learning temporal structure. The first method utilizes comput ational elements that are analogous to temporal combination sensitive neurons in HVc. A network of these elements can learn using local information and lateral inhibition. The second method presents a more general framework which allows a network to discover mixtures of temporal features in a continuous stream of input.