Neural Computation
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
A model of computation in neocortical architecture
Neural Networks - Special issue on organisation of computation in brain-like systems
Fast synchronization of perceptual grouping in laminar visual cortical circuits
Neural Networks - 2004 Special issue Vision and brain
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Multivariable harmonic balance for central pattern generators
Automatica (Journal of IFAC)
Synchronisation of complex networks via partial contraction principle
International Journal of Systems, Control and Communications
Automatica (Journal of IFAC)
Brief paper: Synchronization in networks of identical linear systems
Automatica (Journal of IFAC)
Global robust stability and synchronization of networks with Lorenz-type nodes
IEEE Transactions on Circuits and Systems II: Express Briefs
Cooperative robot control and concurrent synchronization of Lagrangian systems
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Partial synchronization in coupled chemical chaotic oscillators
Journal of Computational and Applied Mathematics
CPG-based control of a turtle-like underwater vehicle
Autonomous Robots
Synchronization and control of complex networks via contraction, adaptation and evolution
IEEE Circuits and Systems Magazine - Special issue on complex networks applications in circuits and systems
Competition through selective inhibitory synchrony
Neural Computation
Collective stability of networks of winner-take-all circuits
Neural Computation
Numerical and analytical methods for synthesis of central pattern generators
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Phase synchronization control of complex networks of Lagrangian systems on adaptive digraphs
Automatica (Journal of IFAC)
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In a network of dynamical systems, concurrent synchronization is a regime where multiple groups of fully synchronized elements coexist. In the brain, concurrent synchronization may occur at several scales, with multiple ''rhythms'' interacting and functional assemblies combining neural oscillators of many different types. Mathematically, stable concurrent synchronization corresponds to convergence to a flow-invariant linear subspace of the global state space. We derive a general condition for such convergence to occur globally and exponentially. We also show that, under mild conditions, global convergence to a concurrently synchronized regime is preserved under basic system combinations such as negative feedback or hierarchies, so that stable concurrently synchronized aggregates of arbitrary size can be constructed. Robustnesss of stable concurrent synchronization to variations in individual dynamics is also quantified. Simple applications of these results to classical questions in systems neuroscience and robotics are discussed.