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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Simple stochastic neuronal models and their parameters

Petr Lansky

Author Affiliations

Institute of Physiology, Academy of Sciences, Videnska 1083, 142 20 Prague 4, Czech Republic

BMC Neuroscience 2009, 10(Suppl 1):P119  doi:10.1186/1471-2202-10-S1-P119

The electronic version of this article is the complete one and can be found online at:

Published:13 July 2009

© 2009 Lansky; licensee BioMed Central Ltd.

Poster presentation

The stochastic approach to the problems of computational neuroscience is common due to the apparent randomness of neuronal behavior. Many stochastic models of neurons have been proposed and deeply studied. They range from simple statistical descriptors to sophisticated and realistic biophysical models. On their basis, properties of neuronal information transfer are deduced. Simple stochastic neuronal models are investigated in the contribution.

The basic assumptions made on the spiking activity permit to consider spike trains as realizations of a stochastic point processes. Then, having the experimental data, the spike trains or membrane depolarization trajectories, we may ask what was the signal stimulating the neuron producing this sequence of action potentials. For this purpose, the parameters of the models have to be determined. The recent results achieved in both these directions and extending our previous effort [1-7] are summarized.


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  2. Hampel D, Lansky P: On the estimation of the refractory period.

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  3. Lansky P, Greenwood PE: Optimal signal estimation in neuronal models.

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  4. Lansky P, Sanda P, He JF: The parameters of the stochastic leaky integrate-and-fire neuronal model.

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  5. Lansky P, Ditlevsen S: A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models.

    Biol Cybernet 2008, 99:253-262. Publisher Full Text OpenURL

  6. Bibbona E, Lansky P, Sacerdote L, Sirovich R: Errors in estimation of input signal for integrate-and-fire neuronal models.

    Phys Rev E 2008, 78:011918. Publisher Full Text OpenURL

  7. Pawlas Z, Klebanov LB, Prokop M, Lansky P: Parameters of Spike Trains Observed in a Short Time Window.

    Neural Comput 2008, 20:1325-1343. PubMed Abstract | Publisher Full Text OpenURL