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 aim to 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 from a 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.
Highlights
IntroductionThe multi-electrode arrays (MEA) system is becoming an increasingly important tool for investigations of neural activity, both in ex vivo brain tissue (e.g., a hippocampal slice preparation from rat or mouse Egert et al, 2002) and in in vitro neuronal cultures (e.g., from embryonic rodent brain tissue Illes et al, 2014 or human stem cells Heikkilä et al, 2009)
The multi-electrode arrays (MEA) system is becoming an increasingly important tool for investigations of neural activity, both in ex vivo brain tissue and in in vitro neuronal cultures
We aim to describe the spatiotemporal properties of subthreshold fluctuations in the rat hippocampal circuit by applying a mathematical description based on Gaussian statistical field theory to MEA data
Summary
The multi-electrode arrays (MEA) system is becoming an increasingly important tool for investigations of neural activity, both in ex vivo brain tissue (e.g., a hippocampal slice preparation from rat or mouse Egert et al, 2002) and in in vitro neuronal cultures (e.g., from embryonic rodent brain tissue Illes et al, 2014 or human stem cells Heikkilä et al, 2009). This technology permits simultaneous long-term recordings from a fairly large number of extra-cellular electrodes. Many methods have been developed for the detection and sorting of spike events (see e.g., Cotterill et al, 2016, for a recent review), and analysis of the statistical properties of spike trains is one of the major modes of investigating neural activity (see e.g., Rieke et al, 1997, for a pedagogical introduction to this field)
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