Synchrony in Dynamic Networks
From NECSIWiki
An interesting phenomena known as synchrony has been observed in dynamic networks. Synchrony can be defined as emergent behavior across a network of nodes. With our current understanding of networks, synchrony has been attributed to the topology of a network. Within the topology of network, it has been observed that both strong and weak coupling can give rise to synchrony. Aside from the topology the exact mechanism by which this phenomena occurs has not been entirely explained (Wang and Chen 2003). To better articulate the information presented, a few examples of synchrony in complex networks are explained below.
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[edit] Internet Routers
Synchrony has been observed in the context of the internet through the transmission of individual messages between independent routers and the functionality of routers in general. Currently two mechanisms have been discovered that can alleviate sychronization of routers. These mechanisms include adding a random component to the period of transmission of messages and changing the deterministic protocol of the routers (Wang and Chen 2003).
[edit] Sensor Networks
Synchronocity in sensor networks is used as a means to minimize the power consumption. This minimization is possible by means of activating all sensor nodes simultaneously to sample their data and transmit that data to a base station. Several applications for these sensors include multi-hop routing protocols, systems with high data transmission requirements, volcanic monitoring, and seismic analysis of structures (Allen et. al. 2005).
[edit] Neural Networks
Synchrony has also been observed as the simultaneous firing of neurons in regions of the brain. This behavior is thought to be the representation of information of a stimulus with different features. To model this behavior, a randomly connected recurrent network is used with temporal dynamics. The firing of neurons within this model showed sychrony on a variety of scales ranging from very localized activation to synchronous activation amongst the entire network (Tsodyks 2000).
[edit] References
- Wang, X.F., Chen, G., "Complex Networks: Small World, Scale Free and Beyond," IEEE Circuits and Systems, First Quarter 2003 pg. 17.
- Allen, G., Tewari, G., Patel, A., Welsh, M., Nagpal, R. "Firefly Inspired Sensor Network Synchronicity with Realistic Radio Effects" Conference On Embedded Networked Sensor Systems archive, Proceedings of the 3rd international conference on Embedded networked sensor systems San Diego, California, USA pg. 142 - 153 2005.
- Tsodyks, M., Uziel, A., Markram, H. "Synchrony Generation in Recurrent Networks with Frequency-Dependent Synapses" Journal of Neuroscience Vol. 20, pg. 1-5 2000.
