Emergence of stimulus-specific synchronous response through STDP in recurrent neural networks

This paper presents learning simulation results on a balanced recurrent neural network of spiking neurons with a simple implementation of the STDP plasticity rule, whose potentiation and depression e ects compensate. The synaptic weights and delays are randomly set and the network activity, which is a combination of an input signal and a recurrent feedback, is initially strong and irregular. Under a static stimulation, the learning process shapes the initial activity toward a more regular and synchronous response. The response is speci c to this particular stimulus: the network has learned to select by synchrony one arbitrary stimulus from a set of random static stimuli.