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**Harvard**

Henningson, M. och Illes, S. (2017) *Analysis and modeling of subthreshold neural multi-electrode array data by statistical field theory*.

** BibTeX **

@article{

Henningson2017,

author={Henningson, Måns and Illes, S.},

title={Analysis and modeling of subthreshold neural multi-electrode array data by statistical field theory},

journal={Frontiers in Computational Neuroscience},

volume={11},

abstract={Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artifacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aimto develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice froma rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behavior that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurrence of spikes. Another important insight is the importance of correctly separating out certain artifacts from the data before proceeding with the analysis.},

year={2017},

keywords={Hippocampus , Multi-electrode-array , Slice preparation , Statistical field theory , Subthreshold oscillations},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 249361

A1 Henningson, Måns

A1 Illes, S.

T1 Analysis and modeling of subthreshold neural multi-electrode array data by statistical field theory

YR 2017

JF Frontiers in Computational Neuroscience

VO 11

AB Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artifacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aimto develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice froma rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behavior that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurrence of spikes. Another important insight is the importance of correctly separating out certain artifacts from the data before proceeding with the analysis.

LA eng

DO 10.3389/fncom.2017.00026

LK http://dx.doi.org/10.3389/fncom.2017.00026

OL 30