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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access Poster presentation

Modeling large-scale neural network culture interface on very-high density multi-electrode arrays

André Garenne1*, Luca Berdondini2, Milena Koudelka2, Sergio Martinoia3, Frédéric Nagy1 and Gwendal Le Masson1

Author Affiliations

1 INSERM E358, Université Bordeaux 2, 146 rue Léo Saignat, 33077 Bordeaux, France

2 IMT, Université de Neuchâtel, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland

3 DIBE, University of Genoa, Via Opera Pia 11A, 16145, Italy

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BMC Neuroscience 2007, 8(Suppl 2):P177  doi:10.1186/1471-2202-8-S2-P177


The electronic version of this article is the complete one and can be found online at:


Published:6 July 2007

© 2007 Garenne et al; licensee BioMed Central Ltd.

Poster presentation

The use of multi-electrode arrays (MEA) technology is developing in neuroscience fields like neuro-pharmacology [1,2], network plasticity investigation [3-5] or neurological diseases [6] and disorders [7]. Dissociated cultures or slices are now often employed on 60–100 multi-site arrays. Recently, matrixes of several thousands of microelectrodes have been developed in order to gain higher spatial resolution from the cell scale up to large network scale. With the framework of a European Consortium (IDEA Project) we developed a 4096 electrode MEA using the Active Pixel Sensor APS technology as well as a computer model of cortical dissociated cultures grown on this device including the neuron-to-electrode interface. Our goal is to better understand the network mechanisms responsible for recorded activity, and to provide integrated software for Computer Aided Design (CAD) of neural engineering devices. Since it was computationally too heavy to work with thousands of interconnected Hodgkin-Huxley cell models, we chose to implement the Izhikevich model which is known as a good compromise between realistic cellular properties and computation time [8]. Indeed, the classical standard leaky integrate-and-fire cellular model can hardly mimic the rich repertoire of intrinsic cellular properties that can be found in biological substrates. We present here the first recordings of high-density MEAs together with dedicated software which can simulate the complete system composed of the electrode matrix and the biological network grown on top. The first results of these large-scale interconnected networks simulations (size and number of cells similar to those recorded in vitro) are consistent with the first recorded data using our prototype of high-density MEAs: (i) the bursts initiation location varies randomly from one place to another, (ii) their propagation varies with the connectivity and the level of presynaptic firing, (iii) the average bursting frequency with no inhibitory connections is close to 1 Hz and similar activity is obtained with bicucculin treated dissociated culture activity. Finally, (iv) the model burst propagation speed is about 100 mm/s and this value has also been computed on real cultures in our lab. This tool is currently used to optimize the design and to investigate the properties of large-scale MEA devices under development and constitutes an innovative neuro-engineering CAD environment.

References

  1. Chiappalone M, Vato A, Tedesco MB, Marcoli M, Davide F, Martinoia S: Networks of neurons coupled to microelectrode arrays: a neuronal sensory system for pharmacological applications.

    Biosens Bioelectron 2003, 18:627-34. PubMed Abstract | Publisher Full Text OpenURL

  2. Gholmieh G, Courellis S, Fakheri S, Cheung E, Marmarelis V, Baudry M, Berger T: Detection and classification of neurotoxins using a novel short-term plasticity quantification method.

    Biosens Bioelectron 2003, 18:1467-78. PubMed Abstract | Publisher Full Text OpenURL

  3. Arnold FJ, Hofmann F, Bengtson CP, Wittmann M, Vanhoutte P, Bading H: Microelectrode array recordings of cultured hippocampal networks reveal a simple model for transcription and protein synthesis-dependent plasticity.

    J Physiol 2005, 564:3-19. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Bakkum DJ, Shkolnik AC, Ben-Ary G, Gamblen P, DeMarse TB, Potter SM: Removing some 'A' from AI: Embodied Cultured Networks.

    Embodied Artificial Intelligence 2004, 3139:130-145. OpenURL

  5. DeMarse TB, Wagenaar DA, Blau AW, Potter SM: The neurally controlled animat: Biological brains acting with simulated bodies.

    Autonomous Robots 2001, 11:305-10. Publisher Full Text OpenURL

  6. Dunia DG, Volmer R, Suberbielle E, Garenne A, Le Masson G: Use of electrophysiology and proteomics to assess the bases of Borna disease virus interference with neuronal plasticity.

    presented at 7th Meeting of the German Neuroscience Society, Göttingen 2007. OpenURL

  7. Wagenaar DA, Madhavan R, Pine J, Potter SM: Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation.

    J Neurosci 2005, 25:680-688. PubMed Abstract | Publisher Full Text OpenURL

  8. Izhikevich EM: Which model to use for cortical spiking neurons?

    IEEE Trans Neural Netw 2004, 15:1063-1070. PubMed Abstract | Publisher Full Text OpenURL