Detecting Neuronal Communities from Beginning of Activation Patterns

Details Detecting Neuronal Communities from Beginning of Activation Patterns Detecting Neuronal Communities from Beginning of Activation Patterns

2008-01-28

arxiv.org/abs/0801.4269v1

Physics

Physics and Society

9 pages, 7 figures. A working manuscript: suggestions and comments welcomed

Scientific paper

The detection of neuronal communities is addressed with basis on two important concepts from neuroscience: facilitation of neuronal firing and nearly simultaneous beginning of activation of sets of neurons. More specifically, integrate-and-fire complex neuronal networks are activated at each of their nodes, and the dissemination of activation is monitored. As the activation received by each neuron accumulates, its firing gets facilitated. The time it takes for each neuron, other than the source, to receive the first non-zero input (beginning activation time) and the time for it to produce the first spike (beginning spiking time) are identified through simulations. It is shown, with respect to two synthetic and a real-world (\emph{C. elegans}) neuronal complex networks, that the patterns of beginning activation times (and to a lesser extent also of the spiking times) tend to cluster into groups corresponding to communities of neurons in the original complex neuronal network. Such an effect is identified to be a direct consequence of the almost simultaneous activation between the nodes inside the same community in which the source of activation is placed, as well as of the respective trapping of activation implied by the integration of activiation prior to firing. Interestingly, the accumulation of activity and thresholds inside each neuron were found to be essential for constraining the initial activations within each respective community during the transient activation (no clear clusters were observed when using overall activation or spiking rates). In addition to its intrinsic value for neuroscience and structure-dynamics studies, these results confirm the importance of the consideration of transient dynamics in complex systems investigations.

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