2012
Reconstructing Mammalian Sleep Dynamics with Data Assimilation
Sedigh-Sarvestani M, Schiff S, Gluckman B. Reconstructing Mammalian Sleep Dynamics with Data Assimilation. PLOS Computational Biology 2012, 8: e1002788. PMID: 23209396, PMCID: PMC3510073, DOI: 10.1371/journal.pcbi.1002788.Peer-Reviewed Original ResearchConceptsUnscented Kalman filterData assimilationData assimilation frameworkParameter estimation methodNonlinear computational modelSleep-wake regulatory networkAssimilation frameworkUnknown parametersHidden variablesCovariance inflationNoisy variablesSlow dynamicsSparse measurementsComputational modelModel parametersKalman filterModel statesEstimation methodSimulation studyComplex systemsOptimal variablesFilter modelUKF frameworkModel variablesPartial observability
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