TY - JOUR
T1 - Engineering spatiotemporal patterns
T2 - information encoding, processing, and controllability in oscillator ensembles
AU - Bomela, Walter
AU - Singhal, Bharat
AU - Li, Jr Shin
N1 - Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - controllability
KW - nonlinear oscillators
KW - optimal tracking control
KW - phase models
UR - http://www.scopus.com/inward/record.url?scp=85164209267&partnerID=8YFLogxK
U2 - 10.1088/2057-1976/ace0c9
DO - 10.1088/2057-1976/ace0c9
M3 - Article
C2 - 37348467
AN - SCOPUS:85164209267
SN - 2057-1976
VL - 9
JO - Biomedical Physics and Engineering Express
JF - Biomedical Physics and Engineering Express
IS - 4
M1 - 045033
ER -