Self-Organizing Small-World Structure of Neural Networks by STDP Learning Rule
Tomoya Suzuki, Tohru Ikeguchi · Institutional Repositories DataBase (IRDB) · 2009
Spike-timing-dependent plasticity (STDP) learning strengthens or weakens synaptic weights of a neural network, thus the neural network temporally evolves by the STDP rule. By estimating the characteristic path length and clustering coefficient, we examined how the neural network structure changes and synaptic spikes synchronize. Even if the neural networks do not have any initial structure, small-world characteristics emerge; the characteristic path length is as small as that of a random graph, but the clustering coefficient is greater.