title: Discriminating deterministic versus stochastic dynamics in neuronal activity.
abstract: An approach to discriminating deterministic versus stochastic dynamics from neuronal data is presented. Direct tests for determinism are emphasized, as well as using time series with clear physical correlates measured from small ensembles of neurons. Surrogate data are used to provide null hypotheses that the dynamics in our data could be accounted for by linear stochastic systems. Algorithms are given in full, and the analysis of an experimental example is given.
authors: Department of Neurosurgery, Children's National Medical Center, Washington, DC 20010.
authors: S J
authors: SJ
authors: Schiff
authors: T
authors: T
authors: Sauer
authors: T
authors: T
authors: Chang
mesh_terms: D000465:Algorithms; D000818:Animals; D002415:Cats; D004558:Electric Stimulation; D005583:Fourier Analysis; D008959:Models, Neurological; D009132:Muscles / Q000294:innervation; D009474:Neurons / Q000502:physiology; D010525:Peripheral Nerves / Q000502:physiology; D012018:Reflex / Q000502:physiology; D013126:Spinal Nerve Roots / Q000502:physiology; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology*