ArticleBiomedical physics & engineering express2023
Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles.
Article in Biomedical physics & engineering express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Control of Oscillator Networks with Mean-Field Measurement: A Hybrid Open/Closed-Loop Approach.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2026Article
- Data-Driven Pattern Formation in Oscillator Networks Using Partial Observations.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2025Article
- Feedback Control of Coupled Nonlinear Oscillators with Uncertain Parameters.Systems & control letters · 2025Article
- A framework for optimal control of oscillations and synchrony applied to non-linear models of neural population dynamics.Frontiers in computational neuroscience · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The ability to finely manipulate spatiotemporal patterns displayed in neuronal populations is critical for understanding and influencing brain functions, sleep cycles, and neurological pathologies. However, such control tasks are challenged not only by the immense scale but also by the lack of real-time state measurements of neurons in the population, which deteriorates the control performance. In this paper, we formulate the control of dynamic structures in an ensemble of neuron oscillators as a tracking problem and propose a principled control technique for designing optimal stimuli that produce desired spatiotemporal patterns in a network of interacting neurons without requiring feedback information. We further reveal an interesting presentation of information encoding and processing in a neuron ensemble in terms of its controllability property. The performance of the presented technique in creating complex spatiotemporal spiking patterns is demonstrated on neural populations described by mathematically ideal and biophysical models, including the Kuramoto and Hodgkin-Huxley models, as well as real-time experiments on Wein bridge oscillators.
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Registered trials
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