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Spiking Neural Networks

posted Aug 7, 2018, 1:05 AM by MUHAMMAD MUN`IM AHMAD ZABIDI

Fig 1. Machine learning encompasses a range of algorithms.


Spiking neural networks (SNNs) are a different form of neural networks that more closely matches biological neurons. SNNs, which use feed-forward training, have low computational and power requirements (Fig. 2) compared to CNNs.

Fig 2. Leveraging feed-forward training, spiking neural networks have low computational and power requirements compared to CNNs.

SNN models also work differently from CNNs because of their spiking nature. Information flows through CNN models in a wavelike fashion; information is modified by weights associated with the nodes in each network layer. SNNs emit spikes in a somewhat similar fashion, but spikes aren’t always generated at each point, depending on the data.

SNN training and hardware requirements are significantly different from CNNs. There are applications where one is much better than the other and areas where they overlap.

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