2022/11/14 by Joseph Pemberton, Paul Chadderton, Rui Ponte Costa · 1 voice
Computer Science · Neuroscience · #Neural Networks and Reservoir Computing #Neural dynamics and brain function #Vestibular and auditory disorders
paper · pdf · doi:10.1101/2022.11.14.516257
openalex publication_date 2022/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Summary To drive behavior, the cortex must bridge sensory cues with future outcomes. However, the principles by which cortical networks learn such sensory-behavioural transformations remain largely elusive. Here, we posit that the cerebellum assumes a crucial role in driving cortical dynamics, thereby enabling rapid and flexible task acquisition. We introduce a computational model of cerebellar networks which learn to drive cortical networks with task-outcome predictions. First, using sensorimotor tasks we show that cerebellar feedback in the presence of minimal cortical plasticity is suffcient for rapid task acquisition and multiple task switching. Next, we demonstrate that, when trained in working memory tasks, the cerebellum can also underlie the maintenance of cognitive-specific dynamics, explaining a range of optogenetic and behavioural observations. Finally, using our model we introduce a systems consolidation theory in which task information is gradually transferred from the cerebellum to the cortex. In summary, our findings suggest that cortico-cerebellar loops play a pivotal role in task acquisition, switching, and consolidation within the brain.