Laurence Aitchison

This paper develops a computational framework that unifies reward-based and error-based learning by mapping them into a shared action-gradient space, linking them to brain systems like the basal ganglia and cerebellum.

This study develops a recurrent neural network model of a visual cortical hypercolumn optimized for sampling-based probabilistic inference, revealing how cortical dynamics arise from efficient computational functions.