A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.
Kuramoto, Self-entrainment of a population of cou- pled non-linear oscillators, inInternational Symposium on Mathematical Problems in Theoretical Physics, edited by H
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A network of optomechanical oscillators is modeled as a platform for neuromorphic computing, with a demonstration that five nodes in all-to-all coupling can implement an XOR gate.
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Quantifying information flow along a stochastic trajectory
A scalable deep-learning estimator for trajectory-level stochastic information flow is proposed and tested on solvable models, oscillators, and motile cell trajectories.
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Neuromorphic computing with optomechanical oscillators
A network of optomechanical oscillators is modeled as a platform for neuromorphic computing, with a demonstration that five nodes in all-to-all coupling can implement an XOR gate.