神经活动数据的交互式分析

Christian Nowke, Maximilian Schmidt, Sacha Jennifer van Albada, Jochen M. Eppler, Rembrandt Bakker, Markus Diesrnann, B. Hentschel, T. Kuhlen
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引用次数: 38

摘要

计算神经科学的目的是通过建模和模拟来深入了解神经系统的动态和功能。目前的研究利用高性能计算设备的力量,使多尺度模拟捕捉低水平的神经活动和大脑区域之间的大规模相互作用。在本文中,我们描述了一种交互式分析工具,使神经科学家能够从这种模拟中探索数据。这项工作背后的驱动挑战之一是将大脑区域层面的宏观数据与微观模拟结果(如单个神经元的活动)相结合。虽然研究人员主要通过以非交互方式可视化这些数据来验证他们的发现,但最先进的可视化,为科学问题量身定制,但足够通用以适应不同类型的模型,使此类分析能够更有效地执行。这项工作描述了几个可视化设计,构思与领域专家密切合作,为网络模型的分析。我们主要致力于探索神经活动数据,检查大脑区域和群体的连通性,以及可视化跨区域的活动通量。我们在与领域专家进行的案例研究中证明了我们方法的有效性。
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VisNEST — Interactive analysis of neural activity data
The aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scalce interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.
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