2023
Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach
Yu M, Peterson M, Cherukuri V, Hehnly C, Mbabazi-Kabachelor E, Mulondo R, Kaaya B, Broach J, Schiff S, Monga V. Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach. Journal Of Neural Engineering 2023, 20: 10.1088/1741-2552/acd9ee. PMID: 37253355, PMCID: PMC11099590, DOI: 10.1088/1741-2552/acd9ee.Peer-Reviewed Original Research
2019
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
Cherukuri V, Guo T, Schiff S, Monga V. Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors. IEEE Transactions On Image Processing 2019, 29: 1368-1383. PMID: 31562091, PMCID: PMC7335214, DOI: 10.1109/tip.2019.2942510.Peer-Reviewed Original ResearchNetwork architectureDeep learning methodsSuper-resolution taskBrain image databaseFeedback layerImage quality measuresLow-rank structureImage databaseArt resultsTraining dataLearning methodsArt methodsImage priorsCompelling stateKey extensionsOutput imageImage matrixImage structureStructural priorsTractable fashionEnhanced sharpnessDifferentiable approximationImage resolutionProcessing constraintsNetwork
2018
Deep Mr Image Super-Resolution Using Structural Priors
Cherukuri V, Guo T, Schiff S, Monga V. Deep Mr Image Super-Resolution Using Structural Priors. 2014 IEEE International Conference On Image Processing (ICIP) 2018, 00: 410-414. PMID: 30930696, PMCID: PMC6440206, DOI: 10.1109/icip.2018.8451496.Peer-Reviewed Original ResearchConvolutional neural networkImage superresolutionMR Image Super-ResolutionDeep learning methodsSuper-resolution taskBrain image databaseImage Super-ResolutionLow-rank structureImage databaseArt resultsNeural networkLearning methodsTraining imageryImage priorsCompelling stateSuper-ResolutionImage matrixImage structureStructural priorsTractable fashionDifferentiable approximationImage resolutionNetworkMagnetic resonance imagesPromising results
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
2015
Observability and Controllability of Nonlinear Networks: The Role of Symmetry
Whalen A, Brennan S, Sauer T, Schiff S. Observability and Controllability of Nonlinear Networks: The Role of Symmetry. Physical Review X 2015, 5: 011005. PMID: 30443436, PMCID: PMC6234006, DOI: 10.1103/physrevx.5.011005.Peer-Reviewed Original ResearchNonlinear networksMathematical modelPresence of symmetryComplex nonlinear networksRole of symmetryNetworked systemsExplicit symmetryLinear networkSuch subspacesPresent theoryRotational symmetryStructural controllabilityObservabilityReal systemComplex networksSymmetryControllabilityObserver modelNonlinear measuresSuch networksSubspaceModel behaviorNetwork observationsNetworkModel
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
Data assimilation for heterogeneous networks: The consensus set
Sauer T, Schiff S. Data assimilation for heterogeneous networks: The consensus set. Physical Review E 2009, 79: 051909. PMID: 19518482, PMCID: PMC2951269, DOI: 10.1103/physreve.79.051909.Peer-Reviewed Original ResearchConceptsHeterogeneous networksEnsemble Kalman filteringData assimilationDynamical networksNetwork experimentsPhysical networkNonstationary environmentsKalman filteringModel networksNetworkAccurate reconstructionUnobserved variablesConsensus setTrackingFilteringBroad applicabilityImplementationOscillatorSetEnvironment
2004
Electric Field Control of Seizure Propagation: From Theory to Experiment
Richardson K, Schiff S, Gluckman B. Electric Field Control of Seizure Propagation: From Theory to Experiment. AIP Conference Proceedings 2004, 742: 185-196. DOI: 10.1063/1.1846476.Peer-Reviewed Original ResearchMathematical modelSpeed of propagationMathematical solutionElectric fieldElectric field controlEpileptic seizure propagationSuch electric fieldsField controlHigh enough valuesEnough valuesPropagationWavesActivity wavesDimensional networkSolutionModelFieldTheoryLower threshold valueNetworkThreshold valueSpeedParameters
1998
Stochastic resonance in mammalian neuronal networks.
Gluckman B, So P, Netoff T, Spano M, Schiff S. Stochastic resonance in mammalian neuronal networks. Chaos An Interdisciplinary Journal Of Nonlinear Science 1998, 8: 588-598. PMID: 12779762, DOI: 10.1063/1.166340.Peer-Reviewed Original Research
1996
Stochastic Resonance in a Neuronal Network from Mammalian Brain
Gluckman B, Netoff T, Neel E, Ditto W, Spano M, Schiff S. Stochastic Resonance in a Neuronal Network from Mammalian Brain. Physical Review Letters 1996, 77: 4098-4101. PMID: 10062387, DOI: 10.1103/physrevlett.77.4098.Peer-Reviewed Original Research