Spiking neural network promises improved AI

This is the claim of researchers at KAUST in Saudi Arabia whose ‘spiking’ neural network lays the foundation for much improved hardware-based AI computing systems.

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Artificial intelligence is gaining traction in areas including advanced automation, data mining and healthcare. These systems are based on a mathematical artificial neural network (ANN) composed of layers of decision-making nodes. Labelled data is fed into the system to ‘train’ the model to respond a certain way, then the decision-making rules are locked in and the model is put into service on standard computing hardware.

While this method works, it is an approximation of the complex, powerful and efficient neural network that makes up the human brain.

“An ANN is an abstract mathematic model that bears little resemblance to real nervous systems and requires intensive computing power,” said Wenzhe Guo, a Ph.D. student in the research team. “A spiking neural network, on the other hand, is constructed and works in the same way as the biological nervous system and can process information in a faster and more energy-efficient way.”