Synaptic Learning With Augmented Spikes

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.
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