Testing for and estimating latency effects for Poisson and non-Poisson spike trains

Authors:
Valérie Ventura
Affiliations:
Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburg, PA
Venue:
Neural Computation
Year:
2004

Citing 5
Cited 3

Disambiguating different covariation types

Neural Computation
Correlations without synchrony

Neural Computation
The time-rescaling theorem and its application to neural spike train data analysis

Neural Computation
A Spike-Train Probability Model

Neural Computation
Determination of Response Latency and Its Application to Normalization of Cross-Correlation Measures

Neural Computation

A rate and history-preserving resampling algorithm for neural spike trains

Neural Computation
First-spike latency in the presence of spontaneous activity

Neural Computation
Feature extraction from spike trains with Bayesian binning: `Latency is where the signal starts'

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Determining the variations in response latency of one or several neurons to a stimulus is of interest in different contexts. Two common problems concern correlating latency with a particular behavior, for example, the reaction time to a stimulus, and adjusting tools for detecting synchronization between two neurons. We use two such problems to illustrate the latency testing and estimation methods developed in this article. Our test for latencies is a formal statistical test that produces a p-value. It is applicable for Poisson and non-Poisson spike trains via use of the bootstrap. Our estimation method is model free, it is fast and easy to implement, and its performance compares favorably to other methods currently available.