Laboratory of Computational Neuroscience

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About the Laboratory

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:

The Board of Directors of the Organization for Computational Neurosciences (OCNS), organization of the Prague 2015 meeting and of the annual Workshops on Methods of Information Theory in Computational Neuroscience
Organization and committee membership in the biennal Neural Coding workshop series since Prague 1995 (Prague 2012; Versailles 2014; Cologne 2016; Torino 2018; Seattle 2020)
Organization of the Information beyond Shannon 2013 workshop (Prague)
Associate and Guest editorship: Neural Processing Letters; BioSystems; Mathematical Biosciences and Engineering; Frontiers in Integrative Neuroscience

Publications

Lánský; Petr - Polito; F. - Sacerdote; L. Input-output consistency in integrate and fire interconnected neurons. Applied Mathematics and Computation. 2023; 440(1 March)); 127630.

IF = 3.5

Lee; H. - Košťál; Lubomír - Kanzaki; R. - Kobayashi; R. Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons. Biosystems. 2023; 223(January)); 104802.

IF = 2.0

Kortus; Štěpán - Řeháková; Kristýna - Klíma; Martin - Kolcheva; Marharyta - Ladislav; Marek - Langore; Emily - Baráčková; Petra - Netolický; Jakub - Misiachna; Anna - Hemelíková; Katarína - Humpolíčková; Jana - Chalupská; Dominika - Šilhán; Jan - Kaniaková; Martina - Hrčka Krausová; Barbora - Bouřa; Evžen - Zápotocký; Martin - Horák; Martin Subunit-Dependent Surface Mobility and Localization of NMDA Receptors in Hippocampal Neurons Measured Using Nanobody Probes. Journal of Neuroscience. 2023; 43(26); 4755-4774.

IF = 4.4

Bárta; Tomáš - Košťál; Lubomír Shared input and recurrency in neural networks for metabolically efficient information transmission. PLoS Computational Biology. 2024; 20(2); e1011896.

IF = 3.8

Rajdl; Kamil - Košťál; Lubomír Estimation of the instantaneous spike train variability. Chaos Solitons & Fractals. 2023; 177(December); 114280.

IF = 5.3

Tomar; Rimjhim - Smith; Ch. E. - Lánský; Petr A simple neuronal model with intrinsic saturation of the firing frequency. Biosystems. 2022; 222(Dec)); 104780.

IF = 1.6