8 citations · 14 across the 7 of their papers we have counts for
7 papers
Overcoming the Curse of Dimensionality: Structural Connectivity Reconstruction via Pairwise Information Flow in Nonlinear Networks
Kai Chen, Zhong-qi K. Tian, Yifei Chen +3
Inferring structural connectivity from observed dynamics remains a fundamental open problem in complex systems, particularly for nonlinear networks where direct measurements are un…
A Generalized Spiking Locally Competitive Algorithm for Multiple Optimization Problems
Xuexing Du, Zhong-qi K. Tian, Songting Li +1
We introduce a generalized Spiking Locally Competitive Algorithm (LCA) that is biologically plausible and exhibits adaptability to a large variety of neuron models and network conn…
Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction
Zhong-qi K. Tian, Kai Chen, Songting Li +2
The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality mea…
Library-based Fast Algorithm for Simulating the Hodgkin-Huxley Neuronal Networks
Zhong-Qi Kyle Tian, Douglas Zhou
We present a modified library-based method for simulating the Hodgkin-Huxley (HH) neuronal networks. By pre-computing a high resolution data library during the interval of an actio…
Design Fast Algorithms For Hodgkin-Huxley Neuronal Networks
Zhong-Qi Kyle Tian, Douglas Zhou
The stiffness of the Hodgkin-Huxley (HH) equations during an action potential (spike) limits the use of large time steps. We observe that the neurons can be evolved independently b…
Design Efficient Exponential Time Differencing method For Hodgkin-Huxley Neural Networks
Zhong-Qi Kyle Tian, Douglas Zhou
The exponential time differencing (ETD) method allows using a large time step to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural networks. For pulse-coupled…
Digital System Reconstruction by Pairwise Transfer Entropy
Zhong-Qi Kyle Tian, Douglas Zhou, David Cai
Transfer entropy (TE) is an attractive model-free method to detect causality and infer structural connectivity of general digital systems. However it relies on high dimensions used…