Related Experiment Video
Updated: Aug 2, 2026
10:45
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
Published on: May 29, 2017
Trial-to-trial variability and its effect on time-varying dependency between two neurons
Valérie Ventura1, Can Cai, Robert E Kass
1Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA. vventura@stat.cmu.edu
Journal of Neurophysiology
|September 15, 2005
Summary
This study introduces a method to account for trial-to-trial variability in neural spike trains, revealing that apparent neuronal correlations may vanish when this variation is properly adjusted. This impacts our understanding of neural synchrony.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Statistical Neuroscience
Background:
- Joint peristimulus time histograms (JPSTH) and cross-correlograms visualize neuronal correlations over time.
- Trial-to-trial variation in neural activity can obscure true correlation and synchrony effects.
- Estimating time-varying firing rates from limited spikes per trial presents a statistical challenge.
Purpose of the Study:
- To develop a method for estimating time-dependent trial-to-trial variation in neural spike trains.
- To refine statistical tests for neuronal synchrony by accounting for complex excitability effects.
- To assess whether neuronal excitability effects are constant or time-varying and shared between neurons.
Main Methods:
- Decomposition of spike-train variability into stimulus-related and trial-specific components.
- Statistical modeling allowing flexible characterization of stimulus-related variability and parsimonious modeling of trial-specific effects.
- Modification of Bootstrap significance tests to incorporate estimated trial-specific variations.
Main Results:
- A novel method was developed to estimate time-varying trial-to-trial variation in spike trains.
- This method allows for the assessment of time-varying excitability effects between neuronal pairs.
- Analysis of V1 neuron data revealed that statistically significant neuronal dependencies disappeared after adjusting for time-varying trial-to-trial variation.
Conclusions:
- Trial-to-trial variation significantly influences the interpretation of neuronal synchrony.
- Apparent correlations between neurons may be artifacts of uncorrected trial-specific excitability.
- The developed methodology provides a robust framework for analyzing time-varying neuronal interactions.
