New approaches to neuronal coding: using spike train distances to identify the most discriminative neuronal subpopulation
Early Stage Researcher: Eero Satuvuori
Principle Investigators: Thomas Kreuz (Florence, UF – major institution), Andreas Daffertshofer and Bob van Dijk (Amsterdam, VUA – partner institution)
During the last decade measures of spike train (dis)similarity have become an essential tool to characterize neuronal coding. In a typical setup different stimuli are presented repeatedly and a pairwise dissimilarity analysis with spike train distances is carried out in order to evaluate whether neuronal responses to the same stimulus exhibit higher degrees of synchrony than responses to different stimuli.
In this project we will apply three recently developed measures of spike train synchrony to simulated and real data. These measures comprise the ISI-distance, the SPIKE-distance, and SPIKE-synchronization all of which are both time-resolved and parameter-free. One of the main objectives is to develop and apply an algorithm which identifies within a larger neuronal population the one subpopulation which discriminates the presented stimuli best.