Dynamical estimation of neuron and network properties. II: Path integral Monte Carlo methods

DOI10.1007/s00422-012-0487-5zbMath1387.92018OpenAlexW2061834780WikidataQ39625909 ScholiaQ39625909MaRDI QIDQ420956

Henry D. I. Abarbanel, Bryan A. Toth, Mark Kostuk, Daniel Margoliash, C. Daniel Meliza

Publication date: 23 May 2012

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

Full work available at URL: https://doi.org/10.1007/s00422-012-0487-5

zbMATH Keywords

Markov chain Monte Carlodata assimilationneuronal dynamicsion channel properties

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Neural biology (92C20) Computational methods for problems pertaining to biology (92-08)

Related Items (6)

Reduced-Dimension, Biophysical Neuron Models Constructed From Observed Data ⋮ Predicting the behavior of sparsely-sampled systems across neurobiology and epidemiology ⋮ Nonlinear statistical data assimilation for \(\mathrm{HVC}_{\mathrm{RA}}\) neurons in the Avian song system ⋮ Data based quantification of synchronization ⋮ On a framework of data assimilation for hyperparameter estimation of spiking neuronal networks ⋮ Data assimilation methods for neuronal state and parameter estimation

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Effective actions for statistical data assimilation
• Dynamical estimation of neuron and network properties. I: Variational methods
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• Monte Carlo sampling methods using Markov chains and their applications
• Data Assimilation

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