Dynamical Spiking Networks for Distributed Control of Nonlinear Systems

Fuqiang Huang, Shinung Ching

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


A topic of interest in theoretical neuroscience pertains to understanding how the activity of spiking neural networks (SNNs, i.e., networks of neurons in the brain) is decoded towards enabling control actions. For example, networks in the brain, while complex and stochastic, are nevertheless able to produce reliable and robust motor commands (moving limbs, etc.). To investigate how spiking networks might achieve these goals, we approach the issue from an engineering viewpoint and ask whether it is possible to synthesize such networks for a tracking objective for nonlinear systems. Our approach consists of two nested optimization problems. The inner optimization involves linearization of a nonlinear system about a template trajectory, which enables the synthesis of a control signal via linear optimal control design methods. The outer optimization involves tailoring the spiking of the designed network to 'copy' the constructed optimal control signal. Remarkably, these nested optimization problems can be achieved by a single, recurrent spiking network whose dynamics can be specified in the closed form. Features of the network and examples of its performance are highlighted.

Original languageEnglish
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States


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