- Poster presentation
- Open Access
Simple stochastic neuronal models and their parameters
© Lansky; licensee BioMed Central Ltd. 2009
- Published: 13 July 2009
- Recent Result
- Stochastic Model
- Basic Assumption
- Point Process
- Spike Train
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.
- Greenwood PE, Lansky P: Information content in threshold data with non-Gaussian noise. Fluctuation and Noise Letters. 2007, 7: 79-89. 10.1142/S0219477507003702.View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- Lansky P, Greenwood PE: Optimal signal estimation in neuronal models. Neural Comput. 2005, 17: 2240-2257. 10.1162/0899766054615653.PubMedView ArticleGoogle Scholar
- 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.PubMedView ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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.PubMedView ArticleGoogle Scholar
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