ArticleJournal of neuroscience methods2022
Latency correction in sparse neuronal spike trains.
Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Next-Generation Stroke Biomarkers: Bridging the Gap Between Innovation and Translation.Journal of clinical medicine · 2026Review
- Quantifying spike train synchrony and directionality: Measures and Applications.Biological cybernetics · 2026Review
- A novel model of light-induced middle cerebral artery occlusion and recanalization in mice.Communications biology · 2025Article
- Acute ischemic STROKE - from laboratory to the Patient's BED (STROKELABED): A translational approach to reperfusion injury. Study Protocol.Translational neuroscience · 2024Article
- Photothrombotic Middle Cerebral Artery Occlusion in Mice: A Novel Model of Ischemic Stroke.eNeuro · 2023Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. NEW
methodWe propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing.
resultsWe show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. COMPARISON WITH EXISTING METHOD(S): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information.
conclusionsFor any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.
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