The effects of continual spike-timing dependent plasticity (STDP) on the topology of evolving neural networks were assessed. After a period of stabilization, a number of topological features were monitored periodically throughout simulations of network activity to quantify changes in network structure. Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure. At the same time, a substantial set of small three-neuron subgraphs (triads) continued to undergo an array of changes and revealed a dynamic local topology. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization.
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Neural networks (Neurobiology), Neuroplasticity, Self-organizing systems.
Stone, David. "Topological dynamics of spike-timing dependent plastic neural networks." (2013). http://digitalrepository.unm.edu/psy_etds/136