Latency correction in sparse neuronal spike trains

Year: 2022

Authors: Kreuz T., Senocrate F., Cecchini G., Checcucci C., Mascaro ALA., Conti E., Scaglione A., Pavone FS.

Autors Affiliation: Natl Res Council CNR, Inst Complex Syst ISC, Sesto Fiorentino, Italy; Univ Florence, Dept Phys & Astron, Sesto Fiorentino, Italy; Univ Barcelona, Dept Math & Comp Sci, Barcelona, Spain; Univ Florence, European Lab Nonlinear Spect LENS, Sesto Fiorentino, Italy; Natl Res Council CNR, Neurosci Inst, Pisa, Italy; Natl Res Council CNR, Natl Inst Opt INO, Sesto Fiorentino, Italy.

Abstract: Background: In 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 Method: We 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.Results: We 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.Conclusions: For 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.

Journal/Review: JOURNAL OF NEUROSCIENCE METHODS

Volume: 381      Pages from: 109703-1  to: 109703-14

More Information: This project has received funding from the H2020 EXCELLENT SCIENCE -European Research Council (ERC) under grant agreement ID n. 692943 BrainBIT and from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 (HBP SGA2) [Grant recipient: F.S.P.]. This resear ch was supported by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3) [Grant recipient: F.S.P.].
KeyWords: Spike train analysis; Latency; Latency Correction; SPIKE-synchronization; SPIKEorder; Synfire Indicator; Simulated Annealing; Mice; stroke; Rehabilitation
DOI: 10.1016/j.jneumeth.2022.109703

Citations: 2
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