Analysis of single-point stochastic neuronal models.
Main methodology is provided by mathematical modelling based on the theory of stochastic processes and differential equations, including extensive numerical simulations. Advanced statistical analysis of simulated as well as real experimental data is performed in order to estimate biophysically relevant parameters of the studied models.
Information processing in sensory neurons and neuronal models.
Methods of information theory and statistical estimation theory are applied to analyse the neuronal coding efficiency, based on both simulated and experimental data. Of particular interest is the notion of efficient coding hypothesis for insect olfactory sensory neurons, and biophysical modelling of ligand-receptor interaction in pheromone reception.
Publikace
Leváková, Marie - Tamborrino, M. - Košťál, Lubomír - Lánský, Petr
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Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding
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Neural Computation. 2016, roč. 28, 10, p. 2162-2180
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IF = 1.626
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doi
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Lánský, Petr - Sacerdote, L. - Zucca, C.
The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model
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Biological Cybernetics. 2016, roč. 110, 2-3, p. 193-200
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IF = 1.611
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doi
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Košťál, Lubomír - Shinomoto, S.
Efficient information transfer by Poisson neurons
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Mathematical Biosciences and Engineering. 2016, roč. 13, 3, p. 509-520
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IF = 1.006
[ASEP]
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doi
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Košťál, Lubomír - Lánský, Petr
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Coding accuracy on the psychophysical scale
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Scientific Reports. 2016, roč. 6, Mar 29, p. 23810
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IF = 5.228
[ASEP]
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doi
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Košťál, Lubomír
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Stimulus reference frame and neural coding precision
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Journal of Mathematical Psychology. 2016, roč. 71, Apr 2016, p. 22-27
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IF = 1.818
[ASEP]
[
doi
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