期刊
BIOLOGICAL CYBERNETICS
卷 99, 期 4-5, 页码 253-262出版社
SPRINGER
DOI: 10.1007/s00422-008-0237-x
关键词
Ornstein-Uhlenbeck; Statistical inference; Feller process; First-passage times; Maximum likelihood; Moment method; Laplace transform; Fortets integral equation; Interspike intervals
资金
- Danish Medical Research Council
- Lundbeck Foundation
- Center for Neurosciences [LC554, AV0Z50110509]
- Academy of Sciences of the Czech Republic [1ET400110401]
Parameters in diffusion neuronal models are divided into two groups; intrinsic and input parameters. Intrinsic parameters are related to the properties of the neuronal membrane and are assumed to be known throughout the paper. Input parameters characterize processes generated outside the neuron and methods for their estimation are reviewed here. Two examples of the diffusion neuronal model, which are based on the integrate-and-fire concept, are investigated-the Ornstein-Uhlenbeck model as the most common one and the Feller model as an illustration of state-dependent behavior in modeling the neuronal input. Two types of experimental data are assumed-intracellular describing the membrane trajectories and extracellular resulting in knowledge of the interspike intervals. The literature on estimation from the trajectories of the diffusion process is extensive and thus the stress in this review is set on the inference made from the interspike intervals.
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