DSNNs: learning transfer from deep neural networks to spiking neural networks

Deep neural networks (DNNs) have drawn great attention as they perform the state-of-the-art results on many tasks. Compared to DNNs, spiking neural networks (SNNs), which are considered as the new generation of neural networks, fail to achieve comparable performance especially on tasks with large problem sizes. Many previous work tried to close the gap between DNNs and SNNs but used small networks on simple tasks. This work proposes a simple but effective way to construct deep spiking neural networks (DSNNs) by transferring the learned ability of DNNs to SNNs. DSNNs achieve comparable accuracy on large networks and complex datasets. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.