21.1. 2019, Početní neurovědy

PhD project:  Information processing and transmission in neuronal models

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 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 but not a requirement.

Relevant publications:

Dayan, P. and Abbott, L. F., Theoretical Neuroscience: Computational and  Mathematical Modeling of Neural Systems, MIT Press, 2001

Kostal L, Lansky P, McDonnell MD., Metabolic cost of neuronal information in an empirical stimulus-response model, Biological Cybernetics, 107, 355-365 (2013)

Kostal L, Lansky P, Pilarski S., Performance breakdown in optimal stimulus decoding, Journal of Neural Engineering, 12, 036012 (2015)

Supervisor:        Lubomir Kostal, Ph.D.