期刊
IEEE COMMUNICATIONS LETTERS
卷 25, 期 6, 页码 1736-1740出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2021.3050207
关键词
Neuromorphic computing; spiking neural networks
资金
- European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme [725731]
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that process binary and sparse spiking signals. They can be implemented on energy-efficient neuromorphic computing platforms and have been validated for their capabilities in detecting and generating spatial patterns through experiments.
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driven, online, fashion. SNNs can be implemented on neuromorphic computing platforms that are emerging as energy-efficient co-processors for learning and inference. This is the first of a series of three letters that introduce SNNs to an audience of engineers by focusing on models, algorithms, and applications. In this first letter, we first cover neural models used for conventional Artificial Neural Networks (ANNs) and SNNs. Then, we review learning algorithms and applications for SNNs that aim at mimicking the functionality of ANNs by detecting or generating spatial patterns in rate-encoded spiking signals. We specifically discuss ANN-to-SNN conversion and neural sampling. Finally, we validate the capabilities of SNNs for detecting and generating spatial patterns through experiments.
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