Transition from Random to Small-World Neural Networks by STDP Learning Rule

The Spike-Timing-Dependent Plasticity (STDP) learning strengthens or weakens synaptic weights of a neural network according to differences of synaptic spike timings. By the STDP rule, the neural network temporally evolves by learning an input spatiotemporal pattern. In the learning process, estimating a characteristic path length and a clustering coefficient, we examined how a structure of the neural network changes and the synchrony of synaptic spikes occurs. As a result, we found that even if the neural networks do not have any initial characteristic structure, an order emerged which is characterized as a small-world network; the characteristic path length is as small as a random graph, but the clustering coefficient is larger than the random graph.