Researchers said the model eliminated the need for vast numbers of unnecessary connections, improving performance in a more sustainable way without sacrificing accuracy.
Dr Bauer added: “Training many of today’s popular large AI models can consume over a million kilowatt-hours of electricity.
“That simply isn’t sustainable at the rate AI continues to grow.”
An enhanced version called Enhanced Topographical Sparse Mapping goes a step further by introducing a biologically inspired “pruning” process during training.
This is similar to how the brain gradually refines its neural connections as it learns.
The research team is also exploring how the approach could be used in other applications, such as more realistic neuromorphic computers – a computing approach inspired by the human brain’s structure and function.