A sparse H8controller synthesis perspective on the reconfiguration of brain networks*

Ilias Mitrai, Catherine Stamoulis, Prodromos Daoutidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Complex networked systems are the norm in the modern world with the human brain being one of the most complex networks. The control of such systems is a difficult task due to the interactions among the individual elements of the system. In this paper the design of sparse feedback controllers for complex networks is considered. Specifically, an H8 controller synthesis problem with D stability constraints is formulated and solved for networks with different topological features. This formulation allows us to examine tradeoffs between control performance, controller sparsity and speed of closed-loop response. We applied this formulation to synthetic networks and the Macaque visual cortical network, assuming Laplacian node dynamics. The results show that as the requested response becomes faster, the control performance improves, and the feedback gain matrix becomes sparser but with larger non-zero entries. This is analogous to the observation that functional brain networks during high cognitive demand adopt a more efficient but also costlier configuration. This analogy suggests a possible connection between cognitive control and closed-loop control under sparse feedback.

Original languageEnglish (US)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665441971
StatePublished - May 25 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: May 25 2021May 28 2021

Publication series

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


Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans

Bibliographical note

Funding Information:
* This work is supported by NSF OAC (award numbers 1938914 and 1940096). 1 Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55414, USA. 2 Department of Medicine, Harvard Medical School, 3Boston Children‘s Hospital, Boston MA, 02115, USA. P. Daoutidis is the corresponding author. daout001@umn.edu

Publisher Copyright:
© 2021 American Automatic Control Council.


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