15,127 research outputs found
Network Identification for Diffusively-Coupled Systems with Minimal Time Complexity
The theory of network identification, namely identifying the (weighted)
interaction topology among a known number of agents, has been widely developed
for linear agents. However, the theory for nonlinear agents using probing
inputs is less developed and relies on dynamics linearization. We use global
convergence properties of the network, which can be assured using passivity
theory, to present a network identification method for nonlinear agents. We do
so by linearizing the steady-state equations rather than the dynamics,
achieving a sub-cubic time algorithm for network identification. We also study
the problem of network identification from a complexity theory standpoint,
showing that the presented algorithms are optimal in terms of time complexity.
We also demonstrate the presented algorithm in two case studies.Comment: 12 pages, 3 figure
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
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