MXHX7199/SNN-SSTDP
SSTDP is a efficient spiking neural network training framework, which is contributed by Fangxin Liu and Wenbo Zhao.
This framework helps machine learning researchers and neuromorphic engineers train spiking neural networks (SNNs) more efficiently. It takes in neural network architectures and training data, and outputs optimized SNN models ready for deployment or further research. This is for individuals working on brain-inspired AI and low-power computing.
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Use this if you are a researcher or engineer looking to train spiking neural networks efficiently, especially those concerned with spike density and computational cost.
Not ideal if you are looking for a general-purpose deep learning framework for standard artificial neural networks or if you are not familiar with spiking neural network concepts.
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37
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2
Language
Python
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Last pushed
Nov 08, 2021
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