**Research**

Theoretical methods are employed to describe and understand particular processes in neural systems on the level of single cells or populations. The focus is mainly on the neural coding problem, mathematical models of neuronal activity, and biophysical modeling of axon growth and circuit formation. The results are validated by using experimental data and numerical simulations. Information theory, stochastic processes, differential equations and statistics provide the necessary tools.

**We are involved in:**

## Publications

Lee; H. - Košťál; Lubomír - Kanzaki; R. - Kobayashi; R.
*Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons*
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**Biosystems. 2023; 223(January)); 104802**
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IF = 1.6
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doi
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Lánský; Petr - Polito; F. - Sacerdote; L.
*Input-output consistency in integrate and fire interconnected neurons*
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**Applied Mathematics and Computation. 2023; 440(1 March)); 127630**
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IF = 4.0
[ASEP]
[
doi
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Tubikanec; I. - Tamborrino; M. - Lánský; Petr - Buckwar; E.
*Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion*
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**Journal of Computational and Applied Mathematics. 2022; 406(May 1)); 113951**
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IF = 2.4
[ASEP]
[
doi
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Tomar; Rimjhim - Smith; Ch. E. - Lánský; Petr
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*A simple neuronal model with intrinsic saturation of the firing frequency*
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**Biosystems. 2022; 222(Dec)); 104780**
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IF = 1.6
[ASEP]
[
doi
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Tomar; Rimjhim - Košťál; Lubomír
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*Variability and Randomness of the Instantaneous Firing Rate*
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**Frontiers in Computational Neuroscience. 2021; 15(Jun 7)); 620410**
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IF = 3.387
[ASEP]
[
doi
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Tamborrino; M. - Lánský; Petr
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*Shot noise; weak convergence and diffusion approximations*
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**Physica. D. 2021; 418(Apr)); 132845**
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IF = 3.751
[ASEP]
[
doi
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Bárta; Tomáš - Košťál; Lubomír
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*Regular spiking in high-conductance states: The essential role of inhibition*
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**Physical Review E. 2021; 103(2)); 022408**
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IF = 2.707
[ASEP]
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doi
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Rajdl; Kamil - Lánský; Petr - Košťál; Lubomír
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*Fano Factor: A Potentially Useful Information*
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**Frontiers in Computational Neuroscience. 2020; 14(Nov 20)); 569049**
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IF = 2.380
[ASEP]
[
doi
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Christodoulou; Ch. - Košťál; Lubomír - Sacerdote; L.
*Editorial*
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**Biosystems. 2020; 187(Jan)); 104049**
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IF = 1.973
[ASEP]
[
doi
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Ditlevsen; S. - Rubio; A. C. - Lánský; Petr
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*Transient dynamics of Pearson diffusions facilitates estimation of rate parameters*
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**Communications in Nonlinear Science and Numerical Simulation. 2020; 82(Mar)); 105034**
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IF = 4.260
[ASEP]
[
doi
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Ascione; G. - D´Onofrio; G. - Košťál; Lubomír - Pirozzi; E.
*An optimal Gauss-Markov approximation for a process with stochastic drift and applications*
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**Stochastic Processes and their Applications. 2020; 130(11); 6481-6514**
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IF = 1.467
[ASEP]
[
doi
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Tomar; Rimjhim
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*Review: Methods of firing rate estimation*
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**Biosystems. 2019; 183(Sep)); 103980**
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IF = 1.808
[ASEP]
[
doi
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Leváková; Marie - Košťál; Lubomír - Monsempés; Ch. - Lucas; P. - Kobayashi; R.
*Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth*
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**Journal of the Royal Society Interface. 2019; 16(157)); 20190246**
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IF = 3.748
[ASEP]
[
doi
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Košťál; Lubomír - Kobayashi; R.
*Critical size of neural population for reliable information transmission*
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**Physical Review E. 2019; 100(5)); 050401**
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IF = 2.296
[ASEP]
[
doi
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D´Onofrio; Giuseppe - Pirozzi; E.
*Asymptotics of Two-boundary First-exit-time Densities for Gauss-Markov Processes*
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**Methodology and Computing in Applied Probability. 2019; 21(3); 735-752**
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IF = 0.809
[ASEP]
[
doi
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