Stochastic modeling of the neuronal activity in the subthalamic nucleus and model parameter identification from Parkinson patient data

DOI10.1007/S00422-010-0397-3zbMath1266.92014DBLPjournals/bc/BasuGTS10OpenAlexW2006045256WikidataQ33619392 ScholiaQ33619392MaRDI QIDQ2376510

Daniel Graupe, Ishita Basu, Daniela Tuninetti, Konstantin V. Slavin

Publication date: 21 June 2013

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00422-010-0397-3

zbMATH Keywords

stochastic modelingparameter identificationOrnstein-Uhlenbeck processParkinson's diseasesubthalamic nucleusinter-spike interval

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Neural biology (92C20) Medical applications (general) (92C50) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Cites Work

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• The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time
• On the parameter estimation for diffusion models of single neuron's activities. I: Application to spontaneous activities of mesencephalic reticular formation cells in sleep and waking states
• A new integral equation for the evaluation of first-passage-time probability densities
• First-passage-time density and moments of the ornstein-uhlenbeck process
• On the histogram as a density estimator:L 2 theory
• Maximum Likelihood Estimation of Parameters in the Inverse Gaussian Distribution, with Unknown Origin
• Towards model-based control of Parkinson's disease
• Diffusion approximation and first passage time problem for a model neuron

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