English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Spiking neural networks (SNNs) are considered to be more biologically plausible and lower power consuming than traditional artificial neural networks (ANNs). SNNs use discrete spikes as input and output, but how to process and learn these discrete spikes efficiently and accurately still remains a challenging task. Moreover, most existing learning methods are inefficient with complicated neuron dynamics and learning procedures being involved. In this paper, we propose efficient alternatives by firstly introducing a simplified and efficient neuron model. Based on it, we develop two new multi-spike learning rules together with an event-driven scheme being presented to improve the processing efficiency. We show that, with the as-proposed rules, a single neuron can be trained to successfully perform challenging tasks such as multi-category classification and feature extraction. Our learning methods demonstrate a significant robustness against various strong noises. Moreover, experimental results on some real-world classification tasks show that our approaches yield higher efficiency with less requirement on computation resource, highlighting the advantages and potential of spike-based processing and driving more efforts towards neuromorphic computing.

New Efficient Multi-Spike Learning for Fast Processing and Robust Learning