Affordable Access

deepdyve-link deepdyve-link
Publisher Website

Dependence of neuronal correlations on filter characteristics and marginal spike train statistics.

Authors
Type
Published Article
Journal
Neural computation
Publication Date
Volume
20
Issue
9
Pages
2133–2184
Identifiers
DOI: 10.1162/neco.2008.05-07-525
PMID: 18439140
Source
Medline
License
Unknown

Abstract

Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.

Statistics

Seen <100 times