The effect of longer range connections on neuronal network dynamics
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1
Tampere University of Technology, Department of Electronics and Communications Engineering, Finland
The dynamics of neuronal networks are often studied in neuronal cultures measured with microelectrode arrays. However, the spatial structure and the connections in the cultures are hard to determine and control. Thus, it is difficult to inspect, how different spatial structures in the networks affect the network behavior. In this study, we built computational model networks with different connection lengths and analyzed, how longer range connections affect the neuronal network behavior.
The model was based on INEX (Lenk 2011), a probabilistic neuronal network model with excitatory and inhibitory neurons. The topology model is demonstrated in Fig 1. Briefly, the neurons were scattered in 2D or 3D space and then connected inside a varying radius with a specific probability so that the resulting network was approximately 10 % connected. Linear delays were added to the connections: the greater the distance between the neurons, the longer the delay of the action potential. The maximum delays were 15 ms. The simulations were performed using the NEST simulator (Gewaltig and Diesmann 2007). The bursting statistics of the neurons were calculated using the Cumulative Moving Average method (Kapucu et al. 2012). The calculated statistics were the mean spike rate, burst rate, burst duration and number of spikes in burst. For the statistical analysis, 64 neurons were selected from each network, representing the electrodes in a standard in vitro multielectrode recording.
The statistics of the spiking and bursting behavior are presented in Fig 2. The effect of longer range connections was similar in 2D and 3D. The burst duration and number of spikes in burst increased remarkably and almost linearly as longer range connections were included in the network. This implies that the bursts die away fast in the networks with only short connections, when all the neighboring neurons have spiked. As the longer range connections are added, each neuron will get input also from more distant neurons and the bursts will last longer.
In 2D, also the spike and burst rate first increased slightly as the maximum connection length increased and then started to decrease again. The increase of the spike rate is most likely related to the increase of number of spikes in burst. The increase of the burst rate implies that the longer range connections turn individual spikes into bursts more likely than when there are only short connections in the network. After the initial increase, the spike and burst rates decrease slightly, as the connections become longer in both 2D and 3D. This decrease may arise from that the longer range connections make the networks more random. As a conclusion, the longer range connections make the bursts in a neuronal network longer, but do not greatly affect the number of spikes or bursts in the network.
Acknowledgements
This research has been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590.
References
Lenk, K. (2011). “A Simple Phenomenological Neuronal Model with Inhibitory and Excitatory Synapses,” in Advances in Nonlinear Speech Processing, ed. Travieso-González, C. M. and Alonso-Hernández, J. B. 232-238. doi: 10.1007/978-3-642-25020-0_30
Gewaltig, M.-O. and Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpedia, 2:4. doi:10.4249/scholarpedia.1430
Kapucu, F. E., Tanskanen, J. M. A., Mikkonen J. E., Ylä-Outinen L., Narkilahti S. and Hyttinen J. A. K. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Front. Comput. Neurosci. 6:38. doi: 10.3389/fncom.2012.00038
Keywords:
computational model,
spatial topology,
spike train analysis,
in vitro,
burst analysis
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Vornanen
I,
Hyttinen
JA and
Lenk
K
(2014). The effect of longer range connections on neuronal network dynamics.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00009
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Received:
03 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Miss. Kerstin Lenk, Tampere University of Technology, Department of Electronics and Communications Engineering, Tampere, Finland, lenk.kerstin@gmail.com