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
Košťál, Lubomír - Lánský, Petr - Pilarski, Stevan
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Performance breakdown in optimal stimulus decoding
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Journal of Neural Engineering. 2015, Vol. 12, 3, 036012
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IF = 3.295
[ASEP]
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doi
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Košťál, Lubomír - Lánský, Petr
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Coding Accuracy Is Not Fully Determined by the Neuronal Model
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Neural Computation. 2015, Vol. 27, 5, p. 1051-1057
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IF = 2.207
[ASEP]
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doi
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Rajdl, Kamil - Lánský, Petr
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Fano factor estimation
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Mathematical Biosciences and Engineering 2014, roč. 11, 1, p. 105-123. ISSN 1547-1063.
IF = 0.84
[ASEP]
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doi
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Leváková, Marie - Ditlevsen, S. - Lánský, Petr
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Estimating latency from inhibitory input
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Biological Cybernetics 2014, roč. 108, 4, p. 475-493. ISSN 0340-1200.
IF = 1.713
[ASEP]
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doi
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Lánský, Petr - Polito, F. - Sacerdote, L.
The role of detachment of in-links in scale-free networks
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Journal of Physics A-Mathematical and Theoretical 2014, roč. 47, 34, p. 345002. ISSN 1751-8113.
IF = 1.583
[ASEP]
[
doi
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