Abstract
In both experimental and computational neuroscience, it is a frequent task to estimate the degree of synchrony or reliability between two or multiple spike trains. ISI-distance method is one of the most common used methods based on the coefficient of variation, which is parameter free and time scale independent. However, it can only measure regular spiking synchronization well while not good at measuring bursting synchronization. In this paper, an improved method of measuring multiple spike train synchrony is proposed, which can measure both regular and burst spike trains well. Using a simulated neuronal network of Hindmarsh–Rose neurons, it is demonstrated that the performance of our methods in distinguish different levels of multiple neuron spike train synchrony performs as well as previous methods. We expect that the method proposed in this paper will be an overall tool for measuring the synchrony of all kinds of spike trains and applications.
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