Learning to classify complex patterns using a VLSI network of spiking neurons

We propose a compact, low power VLSI network of spiking neurons which can learn to classify complex patterns of mean firing rates online and in real-time. The network of integrate-and-fire neurons is connected by bistable synapses that can change their weight using a local spike-based plasticity mechanism. Learning is supervised by a teacher which provides an extra input to the output neurons during training. The synaptic weights are updated only if the current generated by the plastic synapses does not match the output desired by the teacher (as in the perceptron learning rule). We present experimental results that demonstrate how this VLSI network is able to robustly classify uncorrelated linearly separable spatial patterns of mean firing rates.