Skip to main content
  • Poster presentation
  • Open access
  • Published:

Simple stochastic neuronal models and their parameters

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.

References

  1. Greenwood PE, Lansky P: Information content in threshold data with non-Gaussian noise. Fluctuation and Noise Letters. 2007, 7: 79-89. 10.1142/S0219477507003702.

    Article  Google Scholar 

  2. Hampel D, Lansky P: On the estimation of the refractory period. J Neurosci Meth. 2008, 171: 288-295. 10.1016/j.jneumeth.2008.03.003.

    Article  Google Scholar 

  3. Lansky P, Greenwood PE: Optimal signal estimation in neuronal models. Neural Comput. 2005, 17: 2240-2257. 10.1162/0899766054615653.

    Article  PubMed  Google Scholar 

  4. Lansky P, Sanda P, He JF: The parameters of the stochastic leaky integrate-and-fire neuronal model. J Comput Neurosci. 2006, 21: 211-223. 10.1007/s10827-006-8527-6.

    Article  PubMed  Google Scholar 

  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. 10.1007/s00422-008-0237-x.

    Article  Google Scholar 

  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: 011918-10.1103/PhysRevE.78.011918.

    Article  Google Scholar 

  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. 10.1162/neco.2007.01-07-442.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Petr Lansky.

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Lansky, P. Simple stochastic neuronal models and their parameters. BMC Neurosci 10 (Suppl 1), P119 (2009). https://doi.org/10.1186/1471-2202-10-S1-P119

Download citation

  • Published:

  • DOI: https://doi.org/10.1186/1471-2202-10-S1-P119

Keywords