Examinando por Autor "Toharia, Pablo"

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A Unified Framework for Neuroscience Morphological Data Visualization
(MDPI, 2021) Pastor, Luis; Bayona, Sofia; Brito, Juan P.; Cuevas, María; Fernaud, Isabel; Galindo, Sergio Emilio; García-Cantero, Juan José; González de Quevedo, Francisco; Mata, Susana; Robles, Oscar David; Rodríguez, Angel; Toharia, Pablo; Zdravkovic, Ana
The complexity of the human brain makes its understanding one of the biggest challenges that science is currently confronting. Due to its complexity, the brain has been studied at many different levels and from many disciplines and points of view, using a diversity of techniques for getting meaningful data at each specific level and perspective, producing sometimes data that are difficult to integrate. In order to advance understanding of the brain, scientists need new tools that can speed up this analysis process and that can facilitate integrating research results from different disciplines and techniques. Visualization has proved to be useful in the analysis of complex data, and this paper focuses on the design of visualization solutions adapted to the specific problems posed by brain research. In this paper, we propose a unified framework that allows the integration of specific tools to work together in a coordinated manner in a multiview environment, displaying information at different levels of abstraction and combining schematic and realistic representations. The two use cases presented here illustrate the capability of this approach for providing a visual environment that supports the exploration of the brain at all its organizational levels.
H-Isoefficiency: Scalability Metric for Heterogeneous Systems
(J. Vigo - Aguiar, 2010-06) Bosque, Jose Luis; Robles, Oscar D.; Toharia, Pablo; Pastor, Luis
Scalability is one of the most important features in exascale computing. Most of this systems are heterogeneous and therefore it becomes necessary to develop models and metrics that take into account this heterogeneity. This paper presents a new expression of the isoefficiency function called H-isoefficiency. This function can be applied for both homogeneous and heterogeneous systems and allows to analyze the scalability of a parallel system. Then, as an example, a theoretical a priori analysis of the scalability of Floyd¿s algorithm is presented. Finally a model evaluation which demonstrate the correlation between the theoretical analysis and the experimental results is showed.
SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies
(Frontiers, 2021) Galindo, Sergio E.; Toharia, Pablo; Robles, Oscar D.; Pastor, Luis
Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level. SynCoPa has been conceived for the interactive exploration and analysis of the connectivity elements and paths of simple to medium complexity neuronal circuits at the connectome level. This has been done by providing visual metaphors for synapses and interconnection paths, in combination with the representation of detailed neuron morphologies. SynCoPa could be helpful, for example, for establishing or confirming a hypothesis about the spatial distributions of synapses, or for answering questions about the way neurons establish connections or the relationships between connectivity and morphological features. Last, SynCoPa is easily extendable to include functional data provided, for example, by any of the morphologically-detailed simulators available nowadays, such as Neuron and Arbor, for providing a deep insight into the circuits features prior to simulating it, in particular any analysis where it is important to combine morphology, network topology, and physiology.