2021
Approaches in cooling of resistive coil-based low-field Magnetic Resonance Imaging (MRI) systems for application in low resource settings
Natukunda F, Twongyirwe T, Schiff S, Obungoloch J. Approaches in cooling of resistive coil-based low-field Magnetic Resonance Imaging (MRI) systems for application in low resource settings. BMC Biomedical Engineering 2021, 3: 3. PMID: 33579373, PMCID: PMC7881601, DOI: 10.1186/s42490-021-00048-6.Peer-Reviewed Original ResearchPermanent magnet technologyDedicated cooling systemLow-field magnetic resonance imaging systemsMRI systemCooling systemMagnet technologyResistive coilsMagnetic resonance imaging systemResistive magnetResonance imaging systemLow costHigh constructionMagnetsImage qualityImaging systemSystem longevityStrength capabilitiesInstallationSystemCoilStringent prerequisite
2016
Effects of Symmetry on the Structural Controllability of Neural Networks: A Perspective
Whalen A, Brennan S, Sauer T, Schiff S. Effects of Symmetry on the Structural Controllability of Neural Networks: A Perspective. Proceedings Of The 2010 American Control Conference 2016, 2016: 5785-5790. PMID: 29176923, PMCID: PMC5699861, DOI: 10.1109/acc.2016.7526576.Peer-Reviewed Original ResearchGroup representation theoryStructural controllabilityMan-made networksDynamical systemsOptimal actuatorRepresentation theoryEffect of symmetryControl inputExplicit symmetryCritical actuatorsEngineering systemsComplex networksSymmetryControllabilityMinimum numberActuatorsCoupling structureStructural symmetryNeural networkNetworkRecent workTheorySystemBroad interest
2010
Assimilating Seizure Dynamics
Ullah G, Schiff S. Assimilating Seizure Dynamics. PLOS Computational Biology 2010, 6: e1000776. PMID: 20463875, PMCID: PMC2865517, DOI: 10.1371/journal.pcbi.1000776.Peer-Reviewed Original ResearchConceptsModern control theoryDynamics of networksDynamical systemsControl theoryUnmeasured partDynamical interactionsData assimilationSmall neuronal networksPhysical variablesDynamicsBrain dynamicsComputational modelSeizure dynamicsActual measurementsNeuronal networksMicroenvironment dynamicsObservabilityNetworkVariablesModelTheorySystemMeasurements
2009
Time dependence of stimulation/recording-artifact transfer function estimates for neural interface systems
Chernyy N, Schiff S, Gluckman B. Time dependence of stimulation/recording-artifact transfer function estimates for neural interface systems. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2009, 2009: 1380-1383. PMID: 19964759, PMCID: PMC5502111, DOI: 10.1109/iembs.2009.5334297.Peer-Reviewed Original ResearchConceptsControl systemElectrode interface propertiesLow-frequency electric fieldNeural interface systemInterface propertiesControl outputElectric fieldElectrical potential variationsElectrical currentTransfer function magnitudeImpedance changesSystem stateMeasurement pointsInterface systemFunction magnitudeNeural control systemSimultaneous measurementTime dependencePotential variationPropertiesStimulus artifactTransfer function estimatesSystemFilterCurrent
2007
Kalman filter control of a model of spatiotemporal cortical dynamics
Schiff S, Sauer T. Kalman filter control of a model of spatiotemporal cortical dynamics. Journal Of Neural Engineering 2007, 5: 1. PMID: 18310806, PMCID: PMC2276637, DOI: 10.1088/1741-2560/5/1/001.Peer-Reviewed Original ResearchConceptsNonlinear systemsSpiral wave dynamicsSpatiotemporal cortical dynamicsObserver systemSystem stateExcitable systemsWave dynamicsKalman filteringUnscented KalmanEstimate parametersNonlinear methodsControl signalsFilter controlWave patternsApplied electrical fieldExperimental applicationElectrical fieldDynamicsKalmanParametersSuch resultsModelSystemFilteringSuch approaches
2006
Detecting Coupling in the Presence of Noise and Nonlinearity
Netoff T, Carroll T, Pecora L, Schiff S. Detecting Coupling in the Presence of Noise and Nonlinearity. 2006, 265-282. DOI: 10.1002/9783527609970.ch11.Peer-Reviewed Original Research
1996
Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble
Schiff S, So P, Chang T, Burke R, Sauer T. Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Physical Review E 1996, 54: 6708-6724. PMID: 9965897, DOI: 10.1103/physreve.54.6708.Peer-Reviewed Original ResearchNonlinear predictionNonlinear systemsDynamical interdependenceGeneralized synchronyStochastic drivingNumerical examplesNonidentical parametersLinear cross correlationMutual predictionNonlinear correlationCross correlationStandard analysisIdentical parametersPredictionParametersNeural ensemblesSystemEnsembleNeuronal networksExistenceClassCoupling