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While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...
Identifying key players in coupled individual systems is a fundamental problem in network theory. We investigate synchronizable network-coupled dynamical systems such as high-voltage electric power grids and coupled oscillators on complex networks. We defi ...
American Association for the Advancement of Science (AAAS)2019
In complex network-coupled dynamical systems, two questions of central importance are how to identify the most vulnerable components and how to devise a network making the overall system more robust to external perturbations. To address these two questions ...
This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...
Understanding the underlying structure of building occupant dynamics is crucial to improving the effectiveness and energy efficiency of commercial buildings, as occupants fundamentally drive building design and operation. In current practice, we largely ac ...
We have constructed a fitness parameter, characterizing the intrinsic attractiveness for patents to be cited, from attributes of the associated inventions known at the time a patent is granted. This exogenously obtained fitness is shown to determine the te ...
We show that similarly to the logarithmic mean-velocity profile in wall-bounded turbulence, the landscape topography presents an intermediate region with a logarithmic mean-elevation profile. Such profiles are present in complex topographies with channel b ...
One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechani ...
We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according to a binary property, e.g., according to their opinions or preferences on a t ...
Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...
