论文标题
从其节点的动力信号中推断网络
Inferring a network from dynamical signals at its nodes
论文作者
论文摘要
我们为从给定的时间依赖性信号从节点的给定时间依赖的信号推断出未知网络的拓扑的困难反问题提供了近似解决方案。例如，我们测量来自大脑中个体神经元的信号，并推断它们如何相互连接。我们使用最大口径作为推理原理。高维数据的组合挑战使用对成对耦合的两个不同的近似值处理。我们展示了两个原理证明：在非线性遗传拨动开关电路中，在玩具神经网络中。
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.
