Kaspersky Lab researchers have developed a spiking neural network that includes a sleep phase, a feature described by Andrey Lavrentyev, head of the technology development department at Kaspersky Lab. He explained that during sleep the network re-examines daytime decisions, effectively reanalyzing the data and the choices made in the previous hours.
Lavrentyev noted that the day’s stream of information is absorbed and processed by the system, which then faces urgent decisions that cannot wait. In some cases the first conclusion may not be the optimal one, but the network uses the dream-like phase to consolidate experiences from the day. If more time had been available to review, compare, and contrast the information, the outcome might have shifted. The network, he said, participates after the initial analysis to refine and strengthen more deliberate decisions in what can be described as a dreaming process, learning from those reflections.
According to Lavrentyev, spike-based networks are particularly adept at handling events. They bring memory, attention, and artificial intelligence components into a cohesive framework that could underpin future cognitive systems.
Lavrentyev believes that advancing architectures for spike networks will unlock significant opportunities, including the emergence of self-learning capabilities in networks that adapt through experience and internal rehearsal. This line of thought points toward systems that continually improve their reasoning without requiring constant external retraining.
Readers interested in the trajectory of artificial intelligence will find it valuable to consider what the field predicts about future capabilities, the role of neuromorphic chips, and how such hardware can support more human-like processing. The discussion on spike networks and sleep-inspired learning offers a glimpse into how machines might one day reason with a form of internal rehearsal, combining rapid event-driven processing with slower, reflective cycles. These ideas are part of ongoing explorations into more autonomous and adaptable AI systems.
Earlier work at Sechenov Moscow State Medical University introduced a neural network designed to assess the risk of stroke in ocular blood vessels, illustrating the broad applicability of neural networks in health-related prediction tasks and the potential for neuromorphic approaches to contribute to medical decision support.