Analysis and modeling of subthreshold neural multi-electrode array data by statistical field theory
Artikel i vetenskaplig tidskrift, 2017

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.

Subthreshold oscillations

Multi-electrode-array

Hippocampus

Slice preparation

Statistical field theory

Författare

Måns Henningson

Chalmers, Fysik, Biologisk fysik

S. Illes

Sahlgrenska akademin

Frontiers in Computational Neuroscience

1662-5188 (ISSN)

Vol. 11 26

Ämneskategorier

Fysik

Biologiska vetenskaper

Fundament

Grundläggande vetenskaper

Styrkeområden

Livsvetenskaper och teknik

DOI

10.3389/fncom.2017.00026

Mer information

Senast uppdaterat

2018-03-23