一个模拟和估计复杂动态几何网络状态和功能拓扑的框架
A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks
摘要：
我们提出了一个框架，用于模拟信号在几何网络（即网络，可以映射到几何图形在某些空间）和开发算法，估计（即映射）复杂的动态几何网络的状态和功能拓扑结构。在这个框架内，我们定义了这些网络中通常存在的关键特征，这些特征与生物细胞神经网络特别相关：动力学，信号传导，观察和控制。该框架特别适合于从实验可观察的数据中估计细胞神经网络中的功能连接性，并且已经使用图形处理单元（GPU）高性能计算来实现。在计算上，该框架可以模拟蜂窝网络信令接近或快于实时。我们进一步提出了一个标准的测试集的网络来衡量性能和比较不同的映射算法。
英文摘要：
We present a framework for simulating signal propagation in geometric networks (i.e. networks that can be mapped to geometric graphs in some space) and for developing algorithms that estimate (i.e. map) the state and functional topology of complex dynamic geometric net- works. Within the framework we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: Dynamics, signaling, observation, and control. The framework is particularly well-suited for estimating functional connectivity in cellular neural networks from experimentally observable data, and has been implemented using graphics processing unit (GPU) high performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
