A framework for evaluating pairwise and multiway synchrony among stimulus-driven neurons

Authors:
Ryan C. Kelly;Robert E. Kass
Affiliations:
-;-
Venue:
Neural Computation
Year:
2012

Citing 11
Cited 0

Information-geometric measure for neural spikes

Neural Computation
Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity

Neural Computation
A Spike-Train Probability Model

Neural Computation
Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies

Neural Computation
Model Dependence in Quantification of Spike Interdependence by Joint Peri-Stimulus Time Histogram

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

Neural Computation
Discrete-and continuous-time probabilistic models and algorithms for inferring neuronal up and down states

Neural Computation
Measure of correlation orthogonal to change in firing rate

Neural Computation
Detection of bursts in extracellular spike trains using hidden semi-Markov point process models

Journal of Computational Neuroscience
Local field potentials indicate network state and account for neuronal response variability

Journal of Computational Neuroscience
An L1-regularized logistic model for detecting short-term neuronal interactions

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point-process regression models of individual neuron activity with log-linear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then assess the amount of data needed to reliably detect multiway spiking.