22fb0cee7e1f3bde58293de743871417-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors consider associative learning in networks of spiking neurons, and argue that a form of STDP with postsynaptic hyper-polarization is equivalent to the perceptron learning algorithm. The basic form of STDP proposed by the authors relies on traces (similarly to Morrison, Diesmann & Gerstner, "Phenomenological models of synaptic plasticity based on spike timing", Biol Cybern, 2008, 98, 459-478, which should have been mentioned here), and allows for both potentiation and depression of the synapse. The authors then introduce the perceptron learning rule (PLR) for binary variables, in a form where the weighted sum of inputs is compared to a threshold in order to determine the update. As is well known, the PLR is a supervised learning algorithm requiring a target to be specified at the post-synaptic site.