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This article is part of the supplement: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010

Open Access Poster Presentation

Modeling persistent temporal patterns in dissociated cortical cultures using reservoir computing

Tayfun Gürel123*, Samora Okujeni124, Oliver Weihberger124, Stefan Rotter15 and Ulrich Egert12

Author Affiliations

1 Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Germany

2 Dept. of Microsystems Engineering – IMTEK, Albert-Ludwig University of Freiburg, Germany

3 Faculty of Biology, Albert-Ludwig University of Freiburg, Germany

4 Neurobiology and Biophysics, Faculty of Biology , Albert-Ludwig University of Freiburg, Germany

5 Computational Neuroscience, Faculty of Biology, Albert-Ludwig University of Freiburg, Germany

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BMC Neuroscience 2010, 11(Suppl 1):P42  doi:10.1186/1471-2202-11-S1-P42


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/11/S1/P42


Published:20 July 2010

© 2010 Gürel et al; licensee BioMed Central Ltd.

Poster Presentation

Persistent spatiotemporal patterns have been observed extensively in various neural systems including cortical cultures [1]. Activity in cortical cultures is composed of network-wide bursts of spikes, during which global firing rate increases dramatically. Previously, it has been shown that cultures display persistent temporal patterns that are hierarchically organized and stable over several hours. Fluctuations in the culture activity persistently converge to stable precise temporal patterns, for which these patterns are called dynamic attractors. Temporal structure in network bursts can be clustered into several groups, each of which can be seen as a separate burst type.

A model of a neural system should be able to reproduce the temporal patterns under the same input and/or initial state, which is a minimal requirement for a network-level model to reveal the information encoded in such patterns. Our approach taken here is to employ a generic model (a reservoir network) that displays a rich repertoire of complex spatiotemporal patterns to be matched with the observed biological patterns by parameter tuning. More specifically, we employ an Echo State Network (ESN) [2] with leaky integrator neurons as a modeling tool. Here, we consider cultures of dissociated cortical tissue recorded with microelectrode arrays (MEA) as an example of biological neural networks without specific connectivity and simulate the corresponding burst types based on a cue signal. The cue signal is composed of a snapshot (10 ms) of the individual firing rates recorded at each electrode at burst onset and serves as an indicator of the current dynamic state of the network. A simple readout training of the ESN yields a predictive model of the temporal activity pattern in the global firing rate. The simulated pattern displays a high correlation with the actual one observed in the culture (Figure 1). The model can also be used to visualize the underlying structure in the recorded signals.

thumbnailFigure 1. Comparison of the observed firing rate (solid, blue) and the predicted firing rate (dashed, red) in a selected culture. Light blue shaded regions in the background indicate the intervals, where prediction is done based on the cue signal. The cue signal is the spatial pattern containing the firing rates of all electrodes just 1 time step before the shaded region. The overall correlation coefficient between the predicted and the observed signal is 0.88.

Acknowledgements

This work was supported by the German BMBF (BCCN Freiburg, 01GQ0420).

References

  1. Wagenaar DA, Nadasdy Z, Potter SM: Persistent dynamic attractors in activity patterns of cultured neuronal networks.

    Phys Rev E Stat Nonlin Soft Matter Phys 2006, 73(5 Pt 1):051907. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  2. Jaeger H: The ”echo state” approach to analysing and training recurrent neural networks.

    GMD Report 148, GMD - German National Research Institute for Computer Science 2001. OpenURL