Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather only when meaningful changes occur. This could be highly advantageous, as current AI systems are known to consume large amounts of energy.

While some SNNs introduced in the past achieved encouraging results, they typically struggle to retain useful information (i.e., context) for long periods. This was found to be particularly challenging when the models have only a limited amount of data storage available or are operating under energy constraints.

An artificial neuron made of conductive plastics that can perform advanced functions similar to those of biological nerve cells has been demonstrated by researchers at Linköping University, Sweden.

A new airborne imaging approach can reliably detect unexploded weapons that lie in shallow coastal waters and remain an ongoing hazard to public safety, marine ecosystems and infrastructure worldwide. By combining advanced multispectral sensing with artificial intelligence, the researchers were able to identify underwater munitions with high confidence, even when they are partially hidden by sediment, biological growth or debris.

Scientists at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science developed and tested the approach and published their findings in the April issue of Frontiers in Marine Science. The study demonstrates that integrating NASA underwater imaging technologies with machine learning enhances detection accuracy while reducing false positives in complex marine environments.

“Unexploded ordnance in shallow waters remains a serious global challenge,” said Ved Chirayath, Vetlesen Endowed Chair of Earth Sciences in the Department of Ocean Sciences, the study’s lead author. “Our results demonstrate a scalable, airborne solution that can help improve detection accuracy and support safer coastal environments.”