Discussing the article: "Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet)"
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Check out the new article: Neural Networks in Trading: Training Deep Spiking Models (SEW-ResNet).
However, despite their impressive biological plausibility and new prospects, spiking models still lagged behind classical neural networks in a number of practical tasks. This was particularly evident with complex datasets that required a high degree of accuracy and depth of analysis. At some point, it became clear that the path to progress lay in integrating deep learning concepts into the architecture of spiking models. This is how the first attempts to adapt ResNet to a spiking format came about. The concept of Spiking ResNet seemed promising — to carry over the residual blocks from the classical architecture and replace the activation functions with spiking neurons. This approach made it possible to apply the time-tested principle of depth and residual connections in a new, event-driven context.
The results were mixed. On the one hand, models converted from classical neural networks demonstrated outstanding accuracy and set a high standard for other solutions. On the other hand, direct training of Spiking ResNet revealed several issues: a decline in quality as the network depth increases, vanishing and exploding gradients, and difficulties in implementing a correct identity mapping. All of this left the challenge of constructing truly deep and stable spiking models unresolved.
Against this backdrop, the emergence of the SEW-ResNet framework, presented in the paper "Deep Residual Learning in Spiking Neural Networks," marked a significant step forward. The main idea was to take a fresh look at the mechanism of residual learning as applied to spiking architectures. The framework's authors proposed the Spike-Element-Wise approach, which makes it possible to implement identity mapping naturally while avoiding key issues with gradients. Practical testing confirmed the validity of the concept. SEW-ResNet demonstrated not only higher accuracy but also the ability to scale as the model depth increases — something that had not been achieved before. Moreover, for the first time, researchers succeeded in building a spiking neural network with a depth of over 100 layers that maintained training stability and outperformed many existing solutions.
Author: Dmitriy Gizlyk