Measures of spike train synchrony

In experimental neuroscience techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years time-resolved measures of spike train synchrony such as the ISI-distance and the SPIKE-distance have been proposed. Here we add the complementary measure SPIKE-synchronization, a sophisticated multivariate coincidence detector with a very intuitive interpretation. In the first Results chapter we present SPIKY, an interactive graphical user interface that facilitates the application of these three time-resolved measures of spike train synchrony to both simulated and real data. SPIKY, which has been optimized with respect to computation speed and memory demand, also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. In the second Results chapter we deal with the very important problem of latency variations in real data. By means of a validated setup we can show that the parameter-free SPIKE-distance outperforms two time-scale dependent standard measures. In summary, in this thesis we provide several important measures and corrections that when applied to the right experimental datasets could potentially lead to an increased understanding of the neural code - the ultimate goal of neuroscience.