13.1. 2020, Laboratory of Computational Neuroscience

Laboratory of Computational Neuroscience

PhD project: Information coding and transmission in neuronal systems

The research in computational neuroscience has a long tradition, marked by the classical Lapicque, McCulloch-Pitts or Hodgkin-Huxley neuronal models. New topics have emerged alongside the traditional modeling approaches and the problem of neuronal coding is now receiving substantial attention. The goal of the PhD project is to quantitatively characterize different aspects of neuronal information processing by employing information theory, signal detection and estimation theory and theory of stochastic processes. The problem includes the analysis of possible coding and decoding mechanisms in individual neurons or populations, and the analysis of stochastic components in the system. Understanding the principles of information processing in biological neurons may help to introduce new algorithms or new generation of hardware that could enhance artificial sensors.

Candidate’s profile (requirements):

We are looking for a motivated candidate with master's degree in mathematics, physics or related fields, or those expecting to obtain their degree this year. Candidates should be fluent in English. Programming skills (R, Python) are an advantage.

Relevant publications:

Barta T, Kostal L (2019) The effect of inhibition on rate code efficiency indicators, PLoS Comput. Biol., 15, e1007545

Kostal L, Kobayashi R (2019) Critical size of neural population for reliable information transmission, Phys.  Rev. E Rapid Commun. 100, 050401(R)

Levakova M, Kostal L, Monsempes C, Jacob V, Lucas P (2018) Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations, PLoS Comput. Biol., 14, e1006586

Kostal L, Lansky P, Stiber M (2018) Statistics of inverse interspike intervals: the instantaneous firing rate revisited, Chaos, 28, 106305

Supervisor: Lubomir Kostal, PhD