Applied system identification
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
The Wilson–Cowan model, 36 years later
Biological Cybernetics
Networks of the Brain
Anatomic and electro-physiologic connectivity of the language system: A combined DTI-CCEP study
Computers in Biology and Medicine
Time Varying Dynamic Bayesian Network for Nonstationary Events Modeling and Online Inference
IEEE Transactions on Signal Processing
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Inference of functional networks--representing the statistical associations between time series recorded from multiple sensors--has found important applications in neuroscience. However, networksexhibiting time-locked activity between physically independent elements can bias functional connectivity estimates employing passive measurements. Here, a perturbative and adaptive method of inferring network connectivity based on measurement and stimulation--so called "evoked network connectivity" is introduced. This procedure, employing a recursive Bayesian update scheme, allows principled network stimulation given a current network estimate inferred from all previous stimulations and recordings. The method decouples stimulus and detector design from network inference and can be suitably applied to a wide range of clinical and basic neuroscience related problems. The proposed method demonstrates improved accuracy compared to network inference based on passive observation of node dynamics and an increased rate of convergence relative to network estimation employing a naïve stimulation strategy.