ArticleProceedings of the ... American Control Conference. American Control Conference2020
Learning to Control Neurons using Aggregated Measurements.
Article in Proceedings of the ... American Control Conference. American Control Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 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
- Data-Driven Control of Neuronal Networks with Population-Level Measurement.Research square · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Controlling a population of neurons with one or a few control signals is challenging due to the severely underactuated nature of the control system and the inherent nonlinear dynamics of the neurons that are typically unknown. Control strategies that incorporate deep neural networks and machine learning techniques directly use data to learn a sequence of control actions for targeted manipulation of a population of neurons. However, these learning strategies inherently assume that perfect feedback data from each neuron at every sampling instant are available, and do not scale gracefully as the number of neurons in the population increases. As a result, the learning models need to be retrained whenever such a change occurs. In this work, we propose a learning strategy to design a control sequence by using population-level aggregated measurements and incorporate reinforcement learning techniques to find a (bounded, piecewise constant) control policy that fulfills the given control task. We demonstrate the feasibility of the proposed approach using numerical experiments on a finite population of nonlinear dynamical systems and canonical phase models that are widely used in neuroscience.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.