Representation learning in cerebellum-like structures

TL;DR

This paper reviews how plasticity within expansion layers of cerebellum-like structures enhances associative learning, highlighting similarities between cerebellum and mushroom body circuits and emphasizing the importance of internal plasticity mechanisms.

Contribution

It systematically compares experimental data from cerebellum and mushroom body circuits, revealing common principles and emphasizing the role of expansion layer plasticity in representation learning.

Findings

Similar interneuron circuits and plasticity mechanisms in cerebellum and mushroom body

Overlap with recent theoretical models of representation learning

Interaction of non-associative and associative plasticity mechanisms

Abstract

Animals use past experiences to adapt future behavior. To enable this rapid learning, vertebrates and invertebrates have evolved analogous neural structures like the vertebrate cerebellum or insect mushroom body. A defining feature of these circuits is a large expansion layer, which re-codes sensory inputs to improve pattern separation, a prerequisite to learn non-overlapping associations between relevant sensorimotor inputs and adaptive changes in behavior. However, classical models of associative learning treat expansion layers as static, assuming that associations are learned through plasticity at the output synapses. Here, we review emerging evidence that also highlights the importance of plasticity within the expansion layer for associative learning. Because the underlying plasticity mechanisms and principles of this representation learning are only emerging, we systematically compare experimental data from two well-studied circuits for expansion coding -- the cerebellum granule layer and the mushroom body calyx. The data indicate remarkably similar interneuron circuits, dendritic morphology and plasticity mechanisms between both systems that hint at more general principles for representation learning. Moreover, the data show strong overlap with recent theoretical advances that consider interneuron circuits and dendritic computations for representation learning. However, they also hint at an interesting interaction of stimulus-induced, non-associative and reinforced, associative mechanisms of plasticity that is not well understood in current theories of representation learning. Therefore, studying expansion layer plasticity will be important to elucidate the mechanisms and full potential of representation learning for behavioral adaptation.