Learning population of spiking neural networks with perturbation of conductances

In this paper a method is presented for learning of spiking neural networks. It is based on perturbation of synaptic conductances. While this approach is known to be model-free, it is also known to be slow, because it applies improvement direction estimates with large variance. Two ideas are analysed to alleviate this problem: First, learning of many networks at the same time instead of one. Second, autocorrelation of perturbations in time. In the experimental study the method is validated on three learning tasks in which information is conveyed with frequency and spike timing.