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Related Experiment Video

Updated: Aug 29, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Latency correction in sparse neuronal spike trains.

Thomas Kreuz¹, Federico Senocrate², Gloria Cecchini³

¹Institute for Complex Systems (ISC), National Research Council (CNR), Sesto Fiorentino, Italy.

Journal of Neuroscience Methods

|September 8, 2022

Summary

This summary is machine-generated.

We developed a new algorithm for multivariate latency correction in neurophysiological data. This method accurately corrects spike timing delays in sparse datasets, improving synchrony analysis for individual spike timing.

Keywords:

Latency Latency Correction Mice Rehabilitation SPIKE-synchronization SPIKEorder Simulated Annealing Spike train analysis Synfire Indicator stroke

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Related Experiment Videos

Last Updated: Aug 29, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Published on: November 12, 2019

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

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Published on: September 5, 2012

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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Published on: June 24, 2015

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Latency in neurophysiological data causes spurious synchrony reduction.
Systematic spike timing shifts require correction for accurate analysis.
Existing methods fail with sparse spike train data.

Purpose of the Study:

To introduce a novel multivariate latency correction algorithm.
To address the challenge of correcting spike timing delays in sparse neurophysiological data.
To preserve individual spike timing information.

Main Methods:

A two-step algorithm involving spike matching.
Distance minimization using simulated annealing for latency correction.
Application to sparse neurophysiological data.

Main Results:

Demonstrated effectiveness on simulated and real calcium imaging data from mice.
Successfully corrected cortical propagation patterns before and after stroke.
Established criteria for predicting algorithm applicability.

Conclusions:

The new algorithm effectively corrects latency in sparse neurophysiological data.
Applicability criteria can be quickly evaluated for any dataset.
Publicly available source code facilitates easy implementation.