Large-scale modeling for simulating multi-electrode array neurochip recordings
-
1
Lausitz University of Applied Sciences, Germany
Very few models to date have been developed to examine inhibitory and excitatory effects as observed in in-vitro neuronal networks. In in-vitro experiments about 10.000 neurons of frontal cortex tissue stemming from embryonic mice are cultivated on a multielectrode array (MEA) neurochip (Johnstone2010). The object was to simulate experimental data and to compare the results with MEA data using statistical methods.
We developed a spiking neuronal model following the Glauber dynamics (Glauber1963, Hertz2011). 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: 1) neurons are active without external input or stimulus as observed in experiments; 2) noise is observed; 3) synapses can be either excitatory or inhibitory; 4) bursting occurs. In order to simulate these properties we assume that the spikes obey an inhomogenous Poisson distribution. 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 fully connected network.
For the simulation a network with 10.000 neurons ran for 10 minutes with time slice Δt = 5 milliseconds. This choice of Δt ensures that the refractory period of real neurons is reflected in the model. Two thousand inhibitory neurons (all synapses of the neuron are inhibitory) with weights between -0.2 and 0 and 8.000 excitatory neurons with weights between 0 and 0.7 were generated. From the generated 10.000 spike trains 52 were chosen randomly and compared to a MEA neurochip recording. For the comparison spike and burst rate (mean and standard deviation) were calculated (Schroeder2008). 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 what is also demonstrated in the spike histogram. Therefore, the INEX model shows potential to simulate data as observed in experiments with MEA neurochips.
References
Johnstone, A.F.M., Gross, G.W., Weiss, D.G., Schroeder, O., Gramowski, A. and Shafer, T.J. (2010): Microelectrode arrays: A physiologically based neurotoxicity testing platform for the 21st century. NeuroToxicology 31, 331-350.
Glauber, R.J. (1963): Time‐Dependent Statistics of the Ising Model. J. Math. Phys. 4, 294 - 307.
Hertz, J. , Roudi, Y. and Tyrcha, J. (2011): Ising Models for Inferring Network Structure From Spike Data. arXiv 1106.1752v1.
Schroeder, O.H.U., Gramowski, A., Jügelt, K., Teichmann, C., Weiss, D.G. (2008): Spike train data analysis of substance- specific network activity: Application to functional screening in preclinical drug development. 6th Int. Meeting on Substrate-Integrated Microelectrodes.
Keywords:
large scale simulation,
multi-electrode array neurochip,
Neurons,
networks and dynamical systems
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)
Citation:
Lenk
K and
Priwitzer
B
(2011). Large-scale modeling for simulating multi-electrode array neurochip recordings.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00215
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
16 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Miss. Kerstin Lenk, Lausitz University of Applied Sciences, Senftenberg, Germany, lenk.kerstin@gmail.com