Optimization of External Stimuli for Populations of Theta Neurons via Mean-Field Feedback Control

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Chertovskih, RA (Author) FEUP; Pogodaev, N (Author) Other; Staritsyn, MV (Author) FEUP; Silva Sewane, JD (Author) Other; Aguiar, AP (Author) FEUP

Conference proceedings International

Title: 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023, Rome, Italy, July 3-6, 2023

Pages: 512-517

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

Rome, 3 July 2023 through 6 July 2023

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-DEN

DOI: 10.1109/codit58514.2023.10284402

Abstract (EN): We study the problem of designing 'robust' external excitations for control and synchronization of an assembly of homotypic harmonic oscillators representing so-called theta neurons. The model of theta neuron (Theta model) captures, in main, the bursting behavior of spiking cells in the brain of biological beings, enduring periodic oscillations of the electric potential in their membrane. Our task is to find an external stimulus (control), which steers all neurons of a given population to their desired phases (i.e., excites/slows down its spiking activity) with the highest probability. Our methodology is the following: The optimization problem at hand is formulated as an optimal mean-field control problem for the local continuity equation in the space of probability measures. To solve this problem numerically, we propose an indirect deterministic descent method based on an exact representation of the increment (infinite-order variation) of the objective functional. We illustrate the modus operandi of the proposed method discuss some aspects of its practical realization and provide some results of numerical experiments. © 2023 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5