In silico study on structure and dynamics in bursting neuronal networks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
|Title of host publication||Neuroscience 2012; 42nd Annual Meeting, New Orleans, USA, October 14-18, 2012|
|Publisher||Society for Neuroscience (SfN)|
|Number of pages||1|
|Publication status||Published - 13 Oct 2012|
|Publication type||A4 Article in a conference publication|
|Event||The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012 - New Orleans, United States|
Duration: 14 Oct 2012 → 18 Oct 2012
|Conference||The 42nd Annual Meeting of the Society for Neuroscience, SFN 2012, New Orleans, LA, USA, 13-17 October 2012|
|Period||14/10/12 → 18/10/12|
We use two neuron models that are applicable to small (N=100) spontaneously bursting networks, namely, an integrate-and-fire model with short-term plasticity (Tsodyks et al., J. Neurosci. 2000) and a more detailed point-neuron model with four ionic and three synaptic currents (Golomb et al., J. Neurophysiol. 2006). We show that when the in-degree is sharp (binomial), the network activity is best predicted by using the clustering coefficient of the underlying graph. By contrast, when a broad in-degree is used (power-law), the maximum eigenvalue of the connectivity matrix becomes dominant in predicting the network activity. The results are consistent across the two neuron models. In our work the neurons are identical by their features, and no input is applied to the network, and hence all statistical difference between the compared networks is caused by the network structure and the network structure only.
The obtained results shed light on the relevance of different aspects of structure. In in vivo applications the full connectome is rarely accessible, but estimates of certain structural measures may be assessed indirectly (Vlachos et al., PLoS Comp. Biol. 2012). Extracting the structure-function relationship in neuronal networks may have implication on both the way experiments are conducted and on how biologically inspired artificial intelligence will be designed in the future.