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The Influence of Confined Space Size on the Temperature Distribution Characteristics of Internal Window Plume from Well-Ventilated Compartment Fires
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Training Spiking Neural Networks for Reinforcement Learning Tasks With Temporal Coding Method
1. Introduction Neuromorphic engineering aims to emulate the dynamics of biological neurons and synapses with silicon circuits and run spiking neural networks (SNNs) to achieve cognitive behaviors (Mead, 1990). SNNs enjoy the advantages of the unique computing architecture of the brain, such as low-power consumption, massive parallelism, and low-latency processing.
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