» Articles » PMID: 9862930

Slow Covariations in Neuronal Resting Potentials Can Lead to Artefactually Fast Cross-correlations in Their Spike Trains

Overview
Journal J Neurophysiol
Specialties Neurology
Physiology
Date 1998 Dec 24
PMID 9862930
Citations 34
Authors
Affiliations
Soon will be listed here.
Abstract

Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains. J. Neurophysiol. 80: 3345-3351, 1998. A model of two lateral geniculate nucleus (LGN) cells, which interact only through slow (tens of seconds) covariations in their resting membrane potentials, is used here to investigate the effect of such covariations on cross-correlograms taken during stimulus-driven conditions. Despite the slow timescale of the interactions, the model generates cross-correlograms with peak widths in the range of 25-200 ms. These bear a striking resemblance to those reported in studies of LGN cells by Sillito et al., which were taken at the time as evidence of a fast spike timing synchronization interaction; the model highlights the possibility that those correlogram peaks may have been caused by a mechanism other than spike synchronization. Slow resting potential covariations are suggested instead as the dominant generating mechanism. How can a slow interaction generate covariogram peaks with a width 100-1,000 times thinner than its timescale? Broad peaks caused by slow interactions are modulated by the cells' poststimulus time histograms (PSTHs). When the PSTHs have thin peaks (e.g., tens of milliseconds), the cross-correlogram peaks generated by slow interactions will also be thin; such peaks are easily misinterpretable as being caused by fast interactions. Although this point is explored here in the context of LGN recordings, it is a general point and applies elsewhere. When cross-correlogram peak widths are of the same order of magnitude as PSTH peak widths, experiments designed to reveal short-timescale interactions must be interpreted with the issue of possible contributions from slower interactions in mind.

Citing Articles

A flexible Bayesian framework for unbiased estimation of timescales.

Zeraati R, Engel T, Levina A Nat Comput Sci. 2023; 2(3):193-204.

PMID: 36644291 PMC: 9835171. DOI: 10.1038/s43588-022-00214-3.


Deconvolution improves the detection and quantification of spike transmission gain from spike trains.

Spivak L, Levi A, Sloin H, Someck S, Stark E Commun Biol. 2022; 5(1):520.

PMID: 35641587 PMC: 9156687. DOI: 10.1038/s42003-022-03450-5.


Attention Enhances the Efficacy of Communication in V1 Local Circuits.

Hembrook-Short J, Mock V, Usrey W, Briggs F J Neurosci. 2018; 39(6):1066-1076.

PMID: 30541911 PMC: 6363925. DOI: 10.1523/JNEUROSCI.2164-18.2018.


Cortical state determines global variability and correlations in visual cortex.

Scholvinck M, Saleem A, Benucci A, Harris K, Carandini M J Neurosci. 2015; 35(1):170-8.

PMID: 25568112 PMC: 4287140. DOI: 10.1523/JNEUROSCI.4994-13.2015.


Temporal correlation mechanisms and their role in feature selection: a single-unit study in primate somatosensory cortex.

Gomez-Ramirez M, Trzcinski N, Mihalas S, Niebur E, Hsiao S PLoS Biol. 2014; 12(11):e1002004.

PMID: 25423284 PMC: 4244037. DOI: 10.1371/journal.pbio.1002004.