Pattern recognition computation in a spiking neural network with temporal encoding and learning

Many conventional methods have been widely studied to solve the pattern recognition task, but most of them lack the biological plausibility. This paper presents a spiking neural network of integrate-and-fire neurons to perform pattern recognition. A biologically plausible supervised synaptic learning rule is used so that neurons can efficiently make a decision. The whole system contains encoding, learning and readout. It can classify complex patterns of activities stored in a vector, as well as the real-world stimuli. We test the performance of the network with digital images from the MNIST and images of alphabetic letters. It turns out to be able to classify these patterns correctly. In addition, the synaptic dynamics is shown to be compatible with many experimental observations on induction of long-term modifications, like spike-timing-dependent plasticity (STDP).