Discussing the article: "Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (Key Components)"

[MetaQuotes]

Check out the new article: Neural Networks in Trading: A Spiking-Semantic Approach to Spatiotemporal Identification (Key Components).

In this article, we took a detailed look at the integration of the SSAM module into the SEW-ResNeXt block, demonstrating how the S3CE-Net framework enables the effective combination of spiking attention with residual blocks. This architecture ensures accurate processing of temporal and spatial data streams and high training stability. The modularity and flexibility of the components make it easier to extend the model and reuse proven methods.

In the practical section of our previous work, we stopped at a crucial stage: implementing the forward- and backward-pass algorithms for the SSAM module within the OpenCL program. It is there, in the OpenCL kernels, that the foundation of the attention mechanism’s behavior is formed — a mechanism capable of working with a stream of spiking events under strict temporal causality. This was a necessary step, because the computational power is concentrated precisely at the OpenCL level: computing attention matrices, applying masks, and propagating gradients.

Today, we are moving up one level — to the main program structure, where the logic governing interaction between all framework modules is defined. Here we create the CNeuronSSAM class, which serves as a full-fledged implementation of the spiking attention layer within the architecture. While on the OpenCL side we were dealing with pure mathematics, we are now talking about an architectural composition that combines low-level computations with high-level neural logic.

Author: Dmitriy Gizlyk