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GMDS 2012: 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

16. - 20.09.2012, Braunschweig

Simulation of multi-electrode array neurochip recordings

Meeting Abstract

  • Kerstin Lenk - Lausitz University of Applied Sciences, Senftenberg, Deutschland
  • Lars Schwabe - University of Rostock, Deutschland
  • Olaf H.-U, Schröder - NeuroProof GmbH, Rostock, Deutschland
  • Barbara Priwitzer - Lausitz University of Applied Sciences, Senftenberg, Deutschland

GMDS 2012. 57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Braunschweig, 16.-20.09.2012. Düsseldorf: German Medical Science GMS Publishing House; 2012. Doc12gmds049

DOI: 10.3205/12gmds049, URN: urn:nbn:de:0183-12gmds0499

Published: September 13, 2012

© 2012 Lenk et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.


Outline

Text

Multi-electrode array (MEA) neurochips are used to examine effects and toxicity of (potential) neuroactive compounds [1]. These biosensors can react to known and unknown substances [2]. Identified substances are stored in a substance database [3] and can be consulted when classifying unknown substances.

In an in vitro experiment approximately 500,000 cells of the frontal cortex of embryonic mice [4] are cultivated on a MEA neurochip [1], [5]. Circa 10,000 neurons and 90,000 glia cells of the total amount survive. The object was to simulate these experimental data and to compare the results with MEA data using statistical methods.

We developed a pulsing neuronal model following the Glauber dynamics [6], [7]. Our model INEX (inhibitory-excitatory) is a cellular automaton whose cells represent neurons with two possible states: ON or OFF. The binary model should show several characteristics: Firstly, neurons are active without external input or stimulus as observed in experiments. Secondly, noise is observed. Thirdly, synapses can be either excitatory or inhibitory and last bursts, i.e. cascades of action potentials, as a characteristic phenomenon of frontal cortex neuronal network cultures shall be generated [8]. In order to simulate these properties we assume that the spikes, i.e. action potentials, obey an inhomogeneous Poisson distribution [9]. The inhomogeneity of the neuronal activity is realized by inhibitory or excitatory synapses of varying strength. The corresponding parameters are called weights. Spike time history is added, i.e. the probability of spike occurring increases following a spike in the previous time slice. We used a sparsely connected network with 1,000 neurons, i.e. 800 excitatory neurons and 200 inhibitory neurons [10] following Dale’s principle [11]. Each of the 1,000 neurons is connected to approximately 100 other neurons [12]. To simulate the addition of an inhibitory substance, like bicuculline, to the neuronal network, we reduced the excitatory weights.

From the generated 1,000 spike trains 20 were chosen randomly and compared to 20 randomly chosen MEA neurochip recordings of frontal cortex tissue of embryonic mice after 28 days in vitro. For the comparison spike and burst describing features were calculated [3]. Additionally the spike rate histogram was plotted.

The results of the simulation show, that spike and burst rate of the model and of MEA experiments correspond which is also demonstrated in the spike histogram. Therefore, the INEX model shows potential to simulate data as observed in experiments with MEA neurochips.


References

1.
Johnstone A, Gross GW, Weiss D, Schroeder, OHU, Gramowski A, Shafer T. Microelectrode arrays. A physiologically based neurotoxicity testing platform for the 21st century. Neurotoxicology. 2010;31(4):331-50. DOI: 10.1016/j.neuro.2010.04.001 External link
2.
Gross GW, Pancrazio JPP. Neuronal Network Biosensors. In: Knopf GK, Bassi AS, editors. Smart Biosensor Technology. Boca Raton: CRC Press; 2006. p. 177-201.
3.
Schroeder OHU, Gramowski A, Jügelt K, Teichmann C, Weiss DG. Spike train data analysis of substance-specific network activity: Application to functional screening in preclinical drug development. In: 6th International Meeting on Substrate-Integrated Microelectrodes; 2008. p. 113-6.
4.
Gramowski A, Juegelt K, Stuewe S, Schulze R, McGregor G, Wartenberg-Demand A, Loock J, Schroeder O, Weiss D. Functional screening of traditional antidepressants with primary cortical neuronal networks grown on multielectrode neurochips. Eur J Neurosci. 2006;24(2):455-65. DOI: 10.1111/j.1460-9568.2006.04892.x External link
5.
Gross G, Rieske E, Kreutzberg G, Meyer A. A new fixed-array multi-microelectrode system designed for long-term monitoring of extracellular single unit neuronal activity in vitro. Neurosci Lett. 1977;6(2-3):101-5.
6.
Glauber RJ. Time-Dependent Statistics of the Ising Model. J Math Phys. 1963;4:294-307.
7.
Hertz J, Roudi Y, Tyrcha J. Ising Models for Inferring Network Structure From Spike Data. arXiv:1106.1752v1; 2011.
8.
Wagenaar D, Pine J, Potter SM. An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neurosc. 2006;7:11. DOI: 10.1186/1471-2202-7-11 External link
9.
Heeger D. Poisson model of spike generation [Internet]. 2000. Available from: http://www.cns.nyu.edu/~david/handouts/poisson.pdf [cited 12.04.2012] External link
10.
Börgers C, Kopell N. Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity. Neural Comput. 2003;15(3):509-38. DOI: 10.1162/089976603321192059 External link
11.
Strata P, Harvey R. Dale’s principle. Brain Res Bull. 1999;50:349-50.
12.
Latham PE, Richmond BJ, Nelson PG, Nirenberg S. Intrinsic Dynamics in Neuronal Networks I. Theory J Neurophysiol. 2000;83:808-27.