Abstract
Analysing correlations between streams of events is an important problem. It arises for example in Neurosciences, when the connectivity of neurons should be inferred from spike trains that record neurons’ individual spiking activity. While recently some approaches for inferring delayed synaptic connections have been proposed, they are limited in the types of connectivities and delays they are able to handle, or require computation-intensive procedures. This paper proposes a faster and more flexible approach for analysing such delayed correlated activity: a statistical Analysis of the Connectivity of spiking Events (ACE), based on the idea of hypothesis testing. It first computes for any pair of a source and a target neuron the inter-spike delays between subsequent source- and target-spikes. Then, it derives a null model for the distribution of inter-spike delays for uncorrelated neurons. Finally, it compares the observed distribution of inter-spike delays to this null model and infers pairwise connectivity based on the Pearson’s \(\chi ^2\) test statistic. Thus, ACE is capable to detect connections with a priori unknown, non-discrete (and potentially large) inter-spike delays, which might vary between pairs of neurons. Since ACE works incrementally, it has potential for being used in online processing. In an experimental evaluation, ACE is faster and performs comparable or better than four baseline approaches, in terms of AUPRC (reported here), F1, and AUROC (reported on our website), for the majority of the 11 evaluated scenarios.
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Notes
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Experiments were performed using a Intel(R) Core(TM) i7-6820HK CPU @ 2.70 GHz, 16 GB RAM.
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Krempl, G., Kottke, D., Pham, T. (2021). Statistical Analysis of Pairwise Connectivity. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_11
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