Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization Göttingen, Germany.
Front Comput Neurosci. 2011 Feb 1;5:3. doi: 10.3389/fncom.2011.00003. eCollection 2011.
Networks of well-known dynamical units but unknown interaction topology arise across various fields of biology, including genetics, ecology, and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence) of individual interactions, but there is usually no direct way to probe for their existence. Here we present an explicit method for reconstructing interaction networks of leaky integrate-and-fire neurons from the spike patterns they exhibit in response to external driving. Given the dynamical parameters are known, the approach works well for networks in simple collective states but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required.
在包括遗传学、生态学和神经科学在内的各个生物学领域，都会出现由知名动力单元组成但交互拓扑未知的网络。此类网络的集体动力学通常对个别交互的存在（或不存在）敏感，但通常没有直接的方法来探测其存在。在此，我们提出了一种从外部驱动下神经元放电模式中重建泄露积分和放电神经元相互作用网络的显式方法。给定已知的动力学参数，该方法适用于简单集体状态下的网络，但也适用于表现出复杂时空放电模式的网络。特别是，不需要放电时间序列的平稳性。
