论文标题
通过学习学习的高性能计算机上的神经科学模型的高参数空间
Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
论文作者
论文摘要
神经科学模型通常具有很高的自由度，并且参数空间内的特定区域只能产生感兴趣的动态。这使得开发工具和策略，以有效地找到这些为了推进大脑研究而高度重视的区域。在过去几年中，在计算神经科学的许多领域，使用数值模拟探索高维参数空间已成为一种经常使用的技术。高性能计算（HPC）可以为当今提供强大的基础架构，以加快探索速度并在合理时期增加对模型行为的一般理解。
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. High performance computing (HPC) can provide today a powerful infrastructure to speed up explorations and increase our general understanding of the model's behavior in reasonable times.
