论文信息 - Statistical Inference for Stochastic Neuronal Models

Statistical Inference for Stochastic Neuronal Models

One of the main problems in experimental neurophysiology is to decide whether a presented stimuli modifies a discharge pattern of a neuron. To solve the problem the neuron spike train is modelled by doubly stochastic Poisson process and filtering theory is applied to estimate the random intensity functions of the process. Two different models are proposed and the methods of statistical inference for evoked neuronal activity are derived on their basis.

Věra Lánská | V. Lánská

[1] Daniel Commenges,et al. Inference about a change point in experimental neurophysiology , 1986 .

[2] Grace L. Yang,et al. On statistical methods in neuronal spike-train analysis , 1978 .

[3] Seigo Kano,et al. MODELING OF NERVOUS SYSTEMS BY DOUBLY STOCHASTIC POISSON PROCESSES , 1982 .

[4] S E Fienberg,et al. Stochastic models for single neuron firing trains: a survey. , 1974, Biometrics.

[5] P. Brémaud. Point processes and queues, martingale dynamics , 1983 .

[6] H. Tuckwell. Poisson Processes in Biology , 1981 .

[7] Shunji Osaki,et al. On a first-passage problem for a cumulative process with exponential decay , 1976 .

[8] P. Brémaud. Point Processes and Queues , 1981 .

[9] René Lefever,et al. Stochastic Nonlinear Systems in Physics, Chemistry and Biology, 8 , 1981 .

[10] Petr Lánský,et al. Inference for the diffusion models of neuronal activity , 1983 .

[11] K. Åström. Introduction to Stochastic Control Theory , 1970 .

[12] van Jan Schuppen,et al. Stochastic filtering theory: A discussion of concepts, methods, and results , 1979 .

[13] Michael Kohlmann,et al. Stochastic Control Theory and Stochastic Differential Systems , 1979 .

[14] P. R. Vittal,et al. Neuronal spike trains with exponential decay. , 1981, Neurological research.