A VLSI spike-driven dynamic synapse which learns only when necessary

We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is highly effective in learning to classify complex stimuli in semi-supervised fashion. The circuits presented are designed in sub-threshold CMOS consuming extremely low power. The pulse-based neural network communicates with the outside world using the address event representation in an asynchronous fashion. We present measurements from a test chip, characterizing all the modules of the circuit and show how they match well with theoretical expectations. We finally demonstrate that the learning mechanism of the synapse is fully functional by stimulating it with Poisson distributed spike trains