SPSNN: <italic>n</italic>th Order Sequence-Predicting Spiking Neural Network

SPSNN: <italic>n</italic>th Order Sequence-Predicting Spiking Neural Network

We introduce a means of harnessing spiking neural networks (SNNs) with rich dynamics as a dynamic hypothesis to learn complex sequences. The proposed SNN is referred to as nth order sequence-predicting SNN (n-SPSNN), which is capable of single-step prediction and sequence-to-sequence prediction, i.e...

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Bibliographic Details
Main Authors: Dohun Kim, Vladimir Kornijcuk, Cheol Seong Hwang, Doo Seok Jeong
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Sequence-predicting spiking neural network
event-driven learning algorithm of locality
sequence learning
single-step prediction
associative recall
Online Access:https://ieeexplore.ieee.org/document/9113274/

Internet

https://ieeexplore.ieee.org/document/9113274/

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