Event Abstract

The Influence of Structural Changes and Population Interactions on the Entropy Based Synchronicity

  • 1 Tampere University of Technology, Computational Biophysics and Imaging Group, Finland
  • 2 Tampere University of Technology, Department of Pervasive Computing, Finland

Motivation Neuronal synchrony is generally defined as the simultaneous activity of neuronal cells or cell assemblies. Most common way to analyze simultaneous activity is to assess simultaneous activities of spikes [1,2], or temporal occurrence of bursts [3,4]. These methods are widely employed especially for in vitro neuronal networks whereas other methods such as frequency/phase coupling between signals [5] are also employed for in vivo synchrony analysis, e.g. from electroencephalograms (EEG). A reason for that is because brain oscillation frequency bands are well-defined in EEG studies as opposed to developing neuronal cultures. However, for in vitro neuronal signals, spike and burst information is not always adequate due to the challenges in recognition of spikes and bursts in recordings, for such cases analyzing synchrony based on temporal activity is not preferable. This issue motivates the method presented in this paper which evaluates synchrony by means of temporal correlations of spectral complexity for neuronal recordings. In this work, we investigate the influence of structural changes in neuronal networks on this suggested synchronicity method. Material and Methods We performed initial tests with different neuronal interactions and network structures based on the computational model introduced by Tsodyks et al [6]. The parameters for neurons and synapses are the same as in [6]. The network was driven with Gaussian current. Three minutes spike trains of the neurons were simulated using the NEST simulator [7] and population activity is formed by accumulating spike time points of each neuron. Then a Sinc kernel is convolved to obtain a continuous function representing the population spike firing continuously. Next we calculated spectral entropy (SE) as described in [8] based on previous work [9]. After SE is calculated for every time window, we obtain time-variant SE(w), w∈[1 N], where N is the total number of windows where SE is calculated as in Fig.1A. The equation in Fig.1A calculates correlation between pairwise time-variant SEs, SE-sub-X and SE-sub-Y, where SE-sub-X bar and SE-sub-Y bar are the sample means of the corresponding SEs and sigma is the standard deviation. Correlations by means of time-variant entropies are calculated for different network parameters, such as direct vs. indirect connections, different levels of connectivity, number of interacted populations. Results An exemplary result is presented in Fig. 1 for interrelations of three neuronal populations with different connectivity levels. The influence of indirect connection (between 1 and 3) on the measured synchrony for the corresponding levels of connectivities (50% and 10%) can be seen in Figure 1B. Discussion We obtained results from 100 simulations for each tested neuronal network structure and connectivity level. Consequently we are able to validate the relations of neuronal interactions statistically. On the other hand since natural neuronal networks are more complex than the ones defined here, further studies including natural neuronal networks are necessary for the evaluation of the method’s feasibility with MEA recordings. Conclusion Method is able to detect different structural changes and connectivity levels. Future studies with more parameters and tests with natural neuronal cultures would be beneficial for method’s development for practical usability. References [1] R. Q. Quiroga, T. Kreuz, and P. Grassberger, “Event synchronization: a simple and fast method to measure synchronicity and time delay patterns,” Physical Review E., vol. 66, no. 4, article 041904, Oct. 2002. DOI:10.1103/PhysRevE.66.041904 [2] S. Schreiber, J. M. Fellous, D. Whitmer, P. Tiesinga, and T. J. Sejnowski, “A new correlation-based measure of spike timing reliability,” Neurocomputing, vol. 52–54, pp. 925–931, June 2003. DOI:10.1016/S0925-2312(02)00838-X [3] A. Zwanenburg, E. Meijer, W. Jennekens, C. van Pul, B. Kramer, and P. Andriessen, ”Automatic detection of burst synchrony in preterm infants,” 2012 annual international conference of the IEEE Engineering in medicine and biology society (EMBC), 2012. DOI: 10.1109/EMBC.2012.6347021 [4] J. V. Selinger, J. J. Pancrazio, and G. W. Gross, “Measuring synchronization in neuronal networks for biosensor applications,” Biosensors and Bioelectronics, vol. 19, no. 7, pp. 675–683, 2004. DOI:10.1016/S0956-5663(03)00267-7 [5] J. M. Palva, S. Palva, and K. Kaila, ”Phase synchrony among neuronal oscillations in the human cortex,” The Journal of Neuroscience, vol. 25, no. 15, pp. 3962–3972, 2005. [6] M. Tsodyks, A. Uziel, H. Markram, “Synchrony generation in recurrent networks with frequency-dependent synapses,” Journal of Neuroscience 20, no. 1 pp. 825-835 (2000). [7] M. -O. Gewaltig, M. Diesmann. “NEST (neural simulation tool),” Scholarpedia 2, no. 4, 1430, 2007. [8] F. E. Kapucu, J. E. Mikkonen, J. Tanskanen, and J. A. Hyttinen,”Quantification and automatized adaptive detection of in vivo and in vitro neuronal bursts based on signal complexity,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4729-4732, 2015. [9] H. Viertiö-Oja, V. Maja, M. Särkelä, P. Talja, N. Tenkanen, H. Tolvanen-Laakso, M. Paloheimo, A. Vakkuri, A. Yli-Hankala, P. Meriläinen, ”Description of the entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module,” Acta. Anaesthesiol. Scand., vol. 48, pp.154–161, 2004. DOI:10.1111/j.0001-5172.2004.00322.x Figure1. (A) Equation for calculating the correlations between time-variant SEs. (B) Tuned connectivities between populations and potential synchrony between the indirectly connected population (blue arrow). (C) Calculated synchrony values between populations.

Figure 1

Acknowledgements

The works of Kapucu and Tanskanen have been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590. The work of Kapucu has also been supported by the Academy of Finland project Bio-integrated Software Development for Adaptive Sensor Networks, n°278882, by Human Spare Parts Project funded by Tekes – the Finnish Funding Agency for Innovation, and by Ella and Georg Ehrnrooth Foundation, Finland. The work of Tanskanen has also been supported by Jane and Aatos Erkko Foundation, Finland, under the project Biological Neuronal Communications and Computing with ICT.

Keywords: entropy, network topology, Neuronal Populations, network synchronicity

Conference: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.

Presentation Type: Poster Presentation

Topic: MEA Meeting 2016

Citation: Kapucu FE, Vornanen I, A. Tanskanen JM, Christophe F and Hyttinen J (2016). The Influence of Structural Changes and Population Interactions on the Entropy Based Synchronicity. Front. Neurosci. Conference Abstract: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays. doi: 10.3389/conf.fnins.2016.93.00053

Received: 22 Jun 2016; Published Online: 24 Jun 2016.

* Correspondence: Dr. Fikret E Kapucu, Tampere University of Technology, Computational Biophysics and Imaging Group, Tampere, Finland, emre.kapucu@biomed.au.dk

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