156 citations · 465 across the 14 of their papers we have counts for
15 papers
Using spike train distances to identify the most discriminative neuronal subpopulation
Eero Satuvuori, Mario Mulansky, Andreas Daffertshofer +1
Background: Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated b…
Measures of spike train synchrony for data with multiple time-scales
Eero Satuvuori, Mario Mulansky, Nebojsa Bozanic +4
Background: Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the I…
Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns
Thomas Kreuz, Eero Satuvuori, Martin Pofahl +1
Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consi…
PySpike - A Python library for analyzing spike train synchrony
Mario Mulansky, Thomas Kreuz
Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial ro…
A guide to time-resolved and parameter-free measures of spike train synchrony
Mario Mulansky, Nebojsa Bozanic, Andreea Sburlea +1
Measures of spike train synchrony have proven a valuable tool in both experimental and computational neuroscience. Particularly useful are time-resolved methods such as the ISI- an…
Optimizing Large-Scale ODE Simulations
Mario Mulansky
We present a strategy to speed up Runge-Kutta-based ODE simulations of large systems with nearest-neighbor coupling. We identify the cache/memory bandwidth as the crucial performan…