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  • Open Access

Influence of external input on waxing and waning of neuronal network oscillations

  • 1Email author,
  • 2,
  • 1,
  • 1 and
  • 1
BMC Neuroscience200910 (Suppl 1) :P257

https://doi.org/10.1186/1471-2202-10-S1-P257

  • Published:

Keywords

  • Frequency Band
  • Experimental Observation
  • Neuronal Network
  • Simulated Period
  • External Input

Introduction

Experimental observations have reported modulation of cortical oscillations as phases of high synchronization (waxing) followed by periods of reduced synchronization (waning) [13]. Although the phenomenon is present in almost all frequency bands, it is still not understood how this is driven. Here we study whether this phenomenon can occur in a network of inhibitory (I) and excitatory (E) cells and what effect external inputs have.

Methods

Using NEURON, we model a network of N e excitatory and N i inhibitory cells such that N e /N i = 4. The cells have a single compartment, and include passive channels and voltage dependent Na+, K+ channels. Synaptic connections are random, projecting GABA synapses from I to I and I to E cells and AMPA synapses from E to E and E to I cells. To stimulate the network, each cell receives a baseline of current and a stream of spikes delivered at random intervals across the simulated period.

Results

We show that in a stable oscillatory network, waxing and waning occurs without the need for other synaptic mechanisms than the spike generating K+ and Na+ channels. The phenomenon can be modulated by changing the characteristics of the external input, such as number of spikes, mean inter-spike interval, randomness and whether E or I cells receive the external input. See figures 1 and 2.
Figure 1
Figure 1

Raster plots of E (top) and I (middle) populations, during waxing and waning of a beta oscillation, and firing rate histograms (bottom).

Figure 2
Figure 2

Wavelet transform of the activity in the E population for the same time period as shown in figure 1.

Declarations

Acknowledgements

The work was supported by the EU grant MC-RTN NEURoVERS-it (019247).

Authors’ Affiliations

(1)
Department of Integrative Neurophysiology, VU University Amsterdam, 1081, HV, The Netherlands
(2)
Department of Artificial Intelligence, University of Groningen, 9700, AB, The Netherlands

References

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