TY - JOUR
T1 - Spiking Neural Networks - Part I
T2 - Detecting Spatial Patterns
AU - Jang, Hyeryung
AU - Skatchkovsky, Nicolas
AU - Simeone, Osvaldo
N1 - Funding Information:
Manuscript received October 26, 2020; revised December 9, 2020; accepted December 27, 2020. Date of publication January 8, 2021; date of current version June 10, 2021. This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731). This work was done when H. Jang was with King’s College London. The associate editor coordinating the review of this letter and approving it for publication was D. Ciuonzo. (Corresponding author: Nicolas Skatchkovsky.) Hyeryung Jang was with the Department of Engineering, King’s College London, London WC2R 2LS, U.K. She is now with the Department of Artificial Intelligence, Dongguk University, Seoul 04620, South Korea (e-mail: hyeryung.jang@dgu.ac.kr).
Publisher Copyright:
© 1997-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Neuromorphic computing
KW - spiking neural networks
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U2 - 10.1109/LCOMM.2021.3050207
DO - 10.1109/LCOMM.2021.3050207
M3 - Article
AN - SCOPUS:85099599071
SN - 1089-7798
VL - 25
SP - 1736
EP - 1740
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 6
M1 - 9317739
ER -