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  • Poster presentation
  • Open Access

Modeling Extracellular Potentials in Microelectrode Array Recordings

  • 1Email author,
  • 1,
  • 2,
  • 3, 4,
  • 2 and
  • 1
BMC Neuroscience201314 (Suppl 1) :P120

https://doi.org/10.1186/1471-2202-14-S1-P120

  • Published:

Keywords

  • Microelectrode Array
  • Extracellular Potential
  • Transmembrane Current
  • Systematic Benchmarking
  • Array Recording

Microelectrode Array (MEA) measurements from in vitro slices has become an important research tool in neuroscience, however the interpretation of such recordings is not always straightforward. We have developed a modeling framework for emulating in vitro MEA recordings that takes into account both the measurement physics of the MEA set-up, and the underlying neural activity of the slice, resulting in simulated data that closely resembles experimental recordings. Our modeling framework may aid interpretation of experimental data by reproducing the experimental procedure in silico, make experimentally testable predictions, and produce test-data for validating various analysis methods such as CSD estimates and spike-sorting algorithms.

Our simulations are separated into two domains; the first step is simulations of neuronal activity in populations of multi-compartment model neurons, and secondly solving the electrostatic forward problem in the extracellular space. For the neuronal simulations we employ LFPy [1], a Python module built upon NEURON's Python interface [2] to obtain the transmembrane currents in every compartment of the model neurons. Then the Finite Element Method (FEM) is used to solve the Poisson equation from electrostatics and calculate the extracellular potentials in the 3D volume including the electrode sites, and test various approximation schemes. Hence, the effects of the electrodes can be assessed together with the impact of inhomogeneities and anisotropies of the extracellular medium in recordings. The approach is in principle applicable to any multicompartment neuron model (from e.g. ModelDB [3]), any neuron number or any MEA electrode set-up.

We will present our modeling framework, together with an investigation of the electrode effects on the measured signals. Then we will go on to present two different applications. Firstly, we have produced spike-sorting test-data to benchmark automated spike-sorting algorithms [4] used on MEA recordings. This project is part of an international coordinated effort where such test-data will be collected and made available at http://spike.g-node.org, allowing exchange of synthetic and experimental test-data with known underlying activity, and systematic benchmarking and comparison of spike-sorting algorithms applied to such data [5]. Secondly we will present a project where we have been studying the LFP signature of single neurons receiving varying, sub-threshold sinusoidal current input measured by MEAs in an acute brain slice setting [6]. The model output is compared to corresponding experimental data, which includes the detailed reconstruction of the excited neuron.

Declarations

Acknowledgements

This work is supported by the Research Council of Norway (NevroNor, eScience, Notur), and the Norwegian and German nodes of the International Neuroinformatics Coordinating Facility (INCF, G-Node).

Authors’ Affiliations

(1)
Dept. of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
(2)
Dept. of Cognitive Neuroscience, Donders Inst. for Brain, Cognition & Behaviour, Radboud University Medical Centre Nijmegen, The Netherlands
(3)
Dept. of Neuroinformatics, Donders Inst. for Brain, Cognition & Behaviour, Radboud University Nijmegen, The Netherlands
(4)
Jülich Research Institute, Germany

References

  1. LFPy. [http://compneuro.umb.no/LFPy]
  2. Hines ML, Davison AP, Muller E: NEURON and Python. Front Neuroinformatics. 2009, 3: 1-12.PubMed CentralView ArticleGoogle Scholar
  3. Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM: ModelDB: A Database to Support Computational Neuroscience. J Comput Neurosci. 2004, 17 (1): 7-11.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Einevoll GT, Franke F, Hagen E, Pouzat C, Harris KD: Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Curr Opin Neurobiol. 2012, 22: 11-17. 10.1016/j.conb.2011.10.001.PubMed CentralView ArticlePubMedGoogle Scholar
  5. The Spike Sorting Evaluation Project. [http://spike.g-node.org]
  6. Bakker R, Schubert D, Levels K, Bezgin G, Bojak I, Kötter R: Classification of cortical microcircuits based on micro-electrode-array data from slices of rat barrel cortex. Neural Networks. 2009, 22: 1159-1168. 10.1016/j.neunet.2009.07.014.View ArticlePubMedGoogle Scholar

Copyright

© Ness et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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