Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Amadeus Maes, Mauricio Barahona, Claudia Clopath

Abstract: Author summary The brain has the ability to learn and execute sequential behaviour on multiple time scales. This behaviour is often compositional: a set of simple behaviours is concatenated to create a complex behaviour. Technological improvements increasingly shine light on the building blocks of compositional behaviour, yet the underlying neural mechanisms remain unclear. Here, we propose a hierarchical model to study the learning and execution of compositional sequences, using bio-plausible neurons and learning rules. We compare the hierarchical model with a serial version of the model. We demonstrate that the hierarchical model is more flexible, efficient and robust by exploiting the compositional nature of the sequences.