Spiking Network Learns Prediction Dimensions

Researchers led by Zenas C. Chao and Yohei Yamada from The University of Tokyo developed a recurrent spiking-network model that learns event identity, timing, and probability together. Published in Communications Biology in 2026, the study used a Multi-Event Expectation Task with 1,000 neurons trained in 100-trial blocks.