ArticleJournal of neuroscience methods2017
Measures of spike train synchrony for data with multiple time scales.
Article in Journal of neuroscience methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Quantifying spike train synchrony and directionality: Measures and Applications.Biological cybernetics · 2026Review
- Timbre Encoding in the Inferior Colliculus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Neural Response Reliability as a Marker of the Transition of Neural Codes along Auditory Pathways.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Chirp sensitivity and vowel coding in the inferior colliculus.Hearing research · 2025Article
- Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data.Scientific reports · 2025Article
- SpikeShip: A method for fast, unsupervised discovery of high-dimensional neural spiking patterns.PLoS computational biology · 2023Article
- Inferring connectivity of an oscillatory network via the phase dynamics reconstruction.Frontiers in network physiology · 2023Review
- Latency correction in sparse neuronal spike trains.Journal of neuroscience methods · 2022Article
- MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data.Neuroinformatics · 2022Article
- An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs.European biophysics journal : EBJ · 2022Article
- Temporal coding of echo spectral shape in the bat auditory cortex.PLoS biology · 2020Article
- A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover's Distance.Frontiers in computational neuroscience · 2019Article
- Faster Repetition Rate Sharpens the Cortical Representation of Echo Streams in Echolocating Bats.eNeuroArticle
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7 authors.
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Abstract
backgroundMeasures 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. NEW
methodIn 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 the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing.
resultsThe adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. COMPARISON WITH EXISTING
methodsWe find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization.
conclusionsWith these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance).
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