Discussing the article: "Neural Networks in Trading: From Transformers to Spiking Neurons (SpikingBrain)"

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Check out the new article: Neural Networks in Trading: From Transformers to Spiking Neurons (SpikingBrain).

The SpikingBrain framework demonstrates a unique approach to data processing: neurons respond only to significant events, effectively filtering out noise. Its event-driven architecture reduces computational costs while preserving key information about price movements. Adaptive thresholds and the ability to use pre-trained modules ensure the model's flexibility and scalability.

If we look at SpikingBrain through the lens of the evolution of neural network concepts, it appears to be a natural step forward — both logical and timely. The Transformer once sparked a true revolution by moving away from cumbersome recurrent structures and introducing an attention mechanism capable of keeping the entire data sequence in focus at once. This versatility has made it the gold standard for language models and a wide range of related tasks. However, versatility also has its downside. An architecture that works perfectly with text quickly becomes overloaded when it encounters the market. After all, most of the time nothing significant happens, and there are too many noisy fluctuations.

This is exactly where SpikingBrain brings a fresh idea to the table. The framework's authors draw on the concept of attention, but shift it into the event-driven domain. In a classic Transformer, attention is distributed across the entire sequence — the model considers everything at once, identifying parts that are more or less significant. In SpikingBrain, attention arises like a flash — an instantaneous response when a threshold level is reached.

This difference is particularly noticeable in the financial market. Quotes can be thought of as a stream of letters that form the words and sentences of the market's history. Unlike natural language, there are a huge number of extra letters, repetitions, and random insertions here. The Transformer is capable of processing them all. But it often does so excessively, like a pedantic teacher who doesn’t overlook a single detail, even a meaningless one. SpikingBrain, on the other hand, acts like an experienced editor, cutting out well-worn phrases and focusing only on what changes the meaning. The result is that the data structure remains streamlined, and the signal is not overloaded with unnecessary information.

Author: Dmitriy Gizlyk