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dc.contributor.authorDura-Bernal, Salvador
dc.contributor.authorLi, Kan
dc.contributor.authorNeymotin, Samuel A
dc.contributor.authorFrancis, Joseph T
dc.contributor.authorPrincipe, Jose C
dc.contributor.authorLytton, William W
dc.date.accessioned2023-04-10T17:08:53Z
dc.date.available2023-04-10T17:08:53Z
dc.date.issued2016-02-09
dc.identifier.citationDura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW. Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Front Neurosci. 2016 Feb 9;10:28. doi: 10.3389/fnins.2016.00028. PMID: 26903796; PMCID: PMC4746359.en_US
dc.identifier.issn1662-4548
dc.identifier.doi10.3389/fnins.2016.00028
dc.identifier.pmid26903796
dc.identifier.urihttp://hdl.handle.net/20.500.12648/8576
dc.description.abstractNeural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
dc.language.isoenen_US
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fnins.2016.00028/fullen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectbiomimeticen_US
dc.subjectinverse modelen_US
dc.subjectkernel adaptive filteringen_US
dc.subjectmusculoskeletal armen_US
dc.subjectneuroprostheticsen_US
dc.subjectneurostimulationen_US
dc.subjectspiking network modelen_US
dc.subjectvirtual armen_US
dc.titleRestoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.en_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleFrontiers in neuroscienceen_US
dc.source.volume10
dc.source.beginpage28
dc.source.endpage
dc.source.countryUnited States
dc.source.countrySwitzerland
dc.description.versionVoRen_US
refterms.dateFOA2023-04-10T17:08:53Z
html.description.abstractNeural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
dc.description.institutionSUNY Downstateen_US
dc.description.departmentNathan Kline Institute for Psychiatric Researchen_US
dc.description.departmentPhysiology and Pharmacologyen_US
dc.description.degreelevelN/Aen_US
dc.identifier.journalFrontiers in neuroscience


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