Main topics of research include:
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.
Organization of recent international conferences:
- Methods of Information Theory in Computational Neuroscience CNS2016 workshop, Jeju, South Korea, 2016
- Organization for Computational Neurosciences annual meeting (CNS*2015 Prague)
- Neuromechanics and integrative motor control workshop, Prague, 2015
- Methods of Information Theory in Computational Neuroscience workshop, Prague, 2015
- Information beyond Shannon 2013 workshop (Prague)
- Neural Coding workshops: 2010 (Limassol); 2012 (Prague); 2014 (Versailles)