Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks

Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks

Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gains can be obtained if decisions are taken as early as possible as a function of the complexity of the input time series....

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Bibliographic Details
Main Authors: Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Entropy
Subjects:
spiking neural networks
conformal prediction
latency adaptivity
Bayesian learning
Online Access:https://www.mdpi.com/1099-4300/26/2/126

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