Relative ordering learning in spiking neural network for pattern recognition

The timing of spikes plays an important role in the information processing of brain. However, for temporal-based learning algorithms, the temporally precise spike as learning target is not able to fit with the variety of stimuli. The performance of spiking neural networks (SNNs) is adversely affected by fixed target spikes. This paper proposes a new learning rule called relative ordering learning (ROL) for SNNs. In contrast to the existing algorithms that drive a neuron to fire or not fire or to fire at precise times, the ROL rule trains the neuron to emit the first spike to respond to the associated stimuli. Without the limitation of specified precise firing times, the ROL rule can adjust output spikes to fit with the feature of input stimuli dynamically and adaptively. Our experiments have demonstrated the great generalization ability of the ROL rule and its robustness against noise. With a few neurons and ultra-low training cost, the network trained by the ROL rule achieved the classification accuracies of 98.1% and 93.8% for training on the Iris and MNIST datasets, respectively, and the accuracies for testing are 94.2% and 90.3%. The comparison with MLP network also shows the great attraction of the SNN trained by the ROL rule as a solution for fast and efficient recognition tasks.