{"paper":{"title":"Measures of spike train synchrony for data with multiple time-scales","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"physics.data-an","authors_text":"Eero Satuvuori, Fleur Zeldenrust, Irene Malvestio, Kerstin Lenk, Mario Mulansky, Nebojsa Bozanic, Thomas Kreuz","submitted_at":"2017-02-17T15:37:46Z","abstract_excerpt":"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 ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time-scale parametric measures, since by adapting to the local firing rate they take into account all the time-scales of a given dataset.\n  New Method: In data containing multiple time-scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time-scales and a more adaptive approach is needed. Here we propose "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05394","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}