Discussing the article: "Neural Networks in Trading: Training Deep Spiking Models (Spike Integration)"

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Check out the new article: Neural Networks in Trading: Training Deep Spiking Models (Spike Integration).

The article presents a practical implementation of the key components of the SEW-ResNet framework using MQL5. The use of dynamic arrays and spiking mechanisms enables flexible model architecture design and efficient processing of financial time series. The proposed solutions demonstrate how SEW-ResNet can optimize computations and improve the extraction of meaningful features.

Today, we are continuing the work we started. But before we move on to implementing the approaches proposed by the authors of the SEW-ResNet framework, let’s take a brief detour and consider another important feature of spiking models — the neuron remaining silent until an event occurs. This property significantly reduces computational costs, since the neuron waits for a signal and does not expend resources processing empty inputs. In the algorithms we have implemented previously, a zero input does not result in any resource savings. The operation of multiplying by zero was performed every time, even though the result was known in advance.

In classical neural network models, this behavior is entirely justified. A zero input is rare — an exception to the rule — and introducing additional checks often only complicates and slows down computation. But spiking models work differently. Here, most inputs are zero at any given moment, since only individual events — short pulses against a background of silence — are significant. Therefore, adding a zero-value check that allows operations involving zeros to be skipped becomes a natural and justified solution.

If we draw a parallel with financial markets, a neuron’s silence resembles the “quiet” on a chart between spikes in trading activity. During periods when the market is calm, each price tick provides little information — and analyzing each one would be a waste of resources. A spiking neuron behaves in a similar way. It remains inactive until it receives a signal carrying meaningful information. As soon as an event occurs (a sharp spike in volume, a breakout, or a news-driven impulse), the neuron reacts instantly. This approach conserves computational resources and improves the network's resilience to noise by filtering out insignificant changes that have no value.

Author: Dmitriy Gizlyk