Symmetry breaking induced by Spike-Timing-Dependent Plasticity in the presence of recurrent connections.

The article focuses on a study of the dynamics of recurrently connected linear Poisson neurons excited by steady external pulse trains with a correlation structure. According to the authors, the input synaptic weights are modified based on additive Spike-Timing-Dependent Plasticity (STDP) that relies upon both rate-based and pairwise-correlation-based changes. It concludes that the presence of recurrent connections impacts upon the learning of the input connections, which relates to the emergence of self-organised maps in neural networks.