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POSTER PRESENTATION Open Access
Modeling persistent temporal patterns
in dissociated cortical cultures using
reservoir computing
Tayfun Gürel
1,2,3*
, Samora Okujeni
1,2,4
, Oliver Weihberger
1,2,4
, Stefan Rotter
1,5
, Ulrich Egert
1,2
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
San Antonio, TX, USA. 24-30 July 2010
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 pat-
terns 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.
* Correspondence: guerel@informatik.uni-freiburg.de
1
Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Germany
Figure 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.
Gürel et al.BMC Neuroscience 2010, 11(Suppl 1):P42
http://www.biomedcentral.com/1471-2202/11/S1/P42
© 2010 Gürel et al; licensee BioMed Central Ltd.
A model of a neural system should be able to repro-
duce the temporal patterns under the same input and/or
initial state, which is a minimal requirement for a net-
work-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 para-
meter tuning. More specifically, we employ an Echo
State Network (ESN) [2] with leaky integrator neurons
as a modeling tool. Here, we consider cultures of disso-
ciated cortical tissue recorded with microelectrode
arrays (MEA) as an example of biological neural net-
works 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.
Acknowledgements
This work was supported by the German BMBF (BCCN Freiburg, 01GQ0420).
Author details
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.
Published: 20 July 2010
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.
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.
doi:10.1186/1471-2202-11-S1-P42
Cite this article as: Gürel et al.: Modeling persistent temporal patterns
in dissociated cortical cultures using reservoir computing. BMC
Neuroscience 2010 11(Suppl 1):P42.
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Gürel et al.BMC Neuroscience 2010, 11(Suppl 1):P42
http://www.biomedcentral.com/1471-2202/11/S1/P42
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