Imagine hearing a familiar sound and expecting something to happen. Before the event arrives, the brain can predict what it will be, when it will occur and how likely it is. Yet computational models often treat these questions separately or use learning methods that are difficult to reconcile with biological circuits. A new study proposes an alternative in which one population of spiking neurons learns all three together.

The research team was led by Associate Professor Zenas C. Chao along with Yohei Yamada, an academic specialist, from the International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan.

The researchers developed a recurrent spiking-network model to test whether a single neural population could learn event identity, timing and probability using local learning rather than backpropagation or a globally broadcast error signal. The study is published in Communications Biology.