Learning State-Dependent Neural Modulation Policies with Bayesian Optimization

Mark J. Connolly; Sang Eon Park; Robert E. Gross

doi:10.1109/EMBC.2019.8856742

Learning State-Dependent Neural Modulation Policies with Bayesian Optimization

Mark J. Connolly, Sang Eon Park, Robert E. Gross

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.

Original language	English (US)
Title of host publication	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6454-6457
Number of pages	4
ISBN (Electronic)	9781538613115
DOIs	https://doi.org/10.1109/EMBC.2019.8856742
State	Published - Jul 2019
Externally published	Yes
Event	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/Territory	Germany
City	Berlin
Period	7/23/19 → 7/27/19

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2019.8856742

Cite this

Connolly, M. J., Park, S. E., & Gross, R. E. (2019). Learning State-Dependent Neural Modulation Policies with Bayesian Optimization. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 6454-6457). Article 8856742 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8856742

Connolly, Mark J. ; Park, Sang Eon ; Gross, Robert E. / Learning State-Dependent Neural Modulation Policies with Bayesian Optimization. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 6454-6457 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{00da5be56dfb4738a99801dbc2dcf59f,

title = "Learning State-Dependent Neural Modulation Policies with Bayesian Optimization",

abstract = "Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.",

author = "Connolly, {Mark J.} and Park, {Sang Eon} and Gross, {Robert E.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",

year = "2019",

month = jul,

doi = "10.1109/EMBC.2019.8856742",

language = "English (US)",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6454--6457",

booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",

address = "United States",

}

Connolly, MJ, Park, SE & Gross, RE 2019, Learning State-Dependent Neural Modulation Policies with Bayesian Optimization. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019., 8856742, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 6454-6457, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 7/23/19. https://doi.org/10.1109/EMBC.2019.8856742

Learning State-Dependent Neural Modulation Policies with Bayesian Optimization. / Connolly, Mark J.; Park, Sang Eon; Gross, Robert E.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 6454-6457 8856742 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Learning State-Dependent Neural Modulation Policies with Bayesian Optimization

AU - Connolly, Mark J.

AU - Park, Sang Eon

AU - Gross, Robert E.

PY - 2019/7

Y1 - 2019/7

N2 - Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.

AB - Neural modulation is becoming a fundamental tool for understanding and treating neurological diseases and their implicated neural circuits. Given that neural modulation interventions have high dimensional parameter spaces, one of the challenges is selecting the stimulation parameters that induce the desired effect. Moreover, the effect of a given set of stimulation parameters may change depending on the underlying neural state. In this study, we investigate and address the state-dependent effect of medial septum optogenetic stimulation on the hippocampus. We found that pre-stimulation hippocampal gamma (33-50Hz) power influences the effect of medial septum optogenetic stimulation on during-stimulation hippocampal gamma power. We then construct a simulation platform that models this phenomenon for testing optimization approaches. We then compare the performance of a standard implementation of Bayesian optimization, along with an extension to the algorithm that incorporates pre-stimulation state to learn a state-dependent policy. The state-dependent algorithm outperformed the standard approach, suggesting that incorporating pre-stimulation can improve neural modulation interventions.

UR - http://www.scopus.com/inward/record.url?scp=85077869659&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85077869659&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2019.8856742

DO - 10.1109/EMBC.2019.8856742

M3 - Conference contribution

C2 - 31947320

AN - SCOPUS:85077869659

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 6454

EP - 6457

BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

Y2 - 23 July 2019 through 27 July 2019

ER -

Connolly MJ, Park SE, Gross RE. Learning State-Dependent Neural Modulation Policies with Bayesian Optimization. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 6454-6457. 8856742. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8856742

Learning State-Dependent Neural Modulation Policies with Bayesian Optimization

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this