The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory-related pathologies like Alzheimer's Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long-Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down-regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB-based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.
Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage
the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.
In this paper, the preconditioning technique of an elliptic Laplace problem in a global circulation ocean model is analyzed. We suggest an inverse preconditioning technique in order to efficiently compute the numerical solution of the elliptic kernel. Moreover, we show how the convergence rate and the performance of the solver are strictly linked to the discretized grid resolution and to the Laplace coefficients of the oceanic model. Finally, we present an easy-toimplement version of the solver on the Graphics Processing Units (GPUs).
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