IEEE Transactions on Automatic Control · 2013 · 87 citations · 53 references
Natural SciencesMathematicsNeuroscienceComputational NeuroscienceDynamical SystemsNeural EngineeringControl TheoryNeural NetworksNeural System ModelingNeuronal DynamicsOptimal ControlSpiking Neural NetworksControlsNeuron DynamicsNeuronal SynchronizationNonlinear OscillatorsOscillatory DynamicsPontryagin Maximum PrinciplePontryagin's Maximum PrincipleSpiking Neuron Models
Synchronization of oscillations is a phenomenon prevalent in natural, social, and engineering systems. Controlling synchronization of oscillating systems is motivated by a wide range of applications from surgical treatment of neurological diseases to the design of neurocomputers. In this paper, we study the control of an ensemble of uncoupled neuron oscillators described by phase models. We examine controllability of such a neuron ensemble for various phase models and, furthermore, study the related optimal control problems. In particular, by employing Pontryagin's maximum principle, we analytically derive optimal controls for spiking single- and two-neuron systems, and analyze the applicability of the latter to an ensemble system. Finally, we present a robust computational method for optimal control of spiking neurons based on pseudospectral approximations. The methodology developed here is universal to the control of general nonlinear phase oscillators.
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The Kuramoto model: A simple paradigm for synchronization phenomena
Juan A. Acebrón, L. L. Bonilla, C. J. Pérez Vicente et al. · Reviews of Modern Physics · 2005 · 3.5K citations · Full text
Natural Sciences, Biology, Physics +16