Cited 2 times in
A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity
DC Field | Value | Language |
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dc.contributor.author | 박해정 | - |
dc.date.accessioned | 2021-09-29T01:32:36Z | - |
dc.date.available | 2021-09-29T01:32:36Z | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1053-8119 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/184421 | - |
dc.description.abstract | The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Academic Press | - |
dc.relation.isPartOf | NEUROIMAGE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Nuclear Medicine (핵의학교실) | - |
dc.contributor.googleauthor | Jiyoung Kang | - |
dc.contributor.googleauthor | Jinseok Eo | - |
dc.contributor.googleauthor | Dong Myeong Lee | - |
dc.contributor.googleauthor | Hae-Jeong Park | - |
dc.identifier.doi | 10.1016/j.neuroimage.2021.117805 | - |
dc.contributor.localId | A01730 | - |
dc.relation.journalcode | J02332 | - |
dc.identifier.eissn | 1095-9572 | - |
dc.identifier.pmid | 33524581 | - |
dc.contributor.alternativeName | Park, Hae Jeong | - |
dc.contributor.affiliatedAuthor | 박해정 | - |
dc.citation.volume | 230 | - |
dc.citation.startPage | 117805 | - |
dc.identifier.bibliographicCitation | NEUROIMAGE, Vol.230 : 117805, 2021-04 | - |
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