Cerebellar granule cell axons support high-dimensional representations

• Published in: Nature neuroscience Vol. 24; no. 8; pp. 1142 - 1150
• Main Authors: Lanore, Frederic, Cayco-Gajic, N Alex, Gurnani, Harsha, Coyle, Diccon, Silver, R Angus
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.08.2021
• Subjects: Acousto-optics; Activity patterns; Animals; Associative learning; Axons; Axons - physiology; Behavior, Animal - physiology; Brain cells; Brain research; Calcium imaging; Cerebellum; Cerebellum - physiology; Cerebral cortex; Circuits; Cortex (somatosensory); Decoders; Female; Granular materials; Imaging, Three-Dimensional - methods; Life Sciences; Locomotion - physiology; Male; Mental representation; Mice; Mice, Transgenic; Motor skill learning; Motor task performance; Neocortex; Neuroimaging; Neurons and Cognition; Perceptual-motor processes; Photons; Physiological aspects; Population; Psychological aspects; Representations; Subspaces; United Kingdom
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/s41593-021-00873-x

In classical theories of cerebellar cortex, high-dimensional sensorimotor representations are used to separate neuronal activity patterns, improving associative learning and motor performance. Recent experimental studies suggest that cerebellar granule cell (GrC) population activity is low-dimensional. To examine sensorimotor representations from the point of view of downstream Purkinje cell 'decoders', we used three-dimensional acousto-optic lens two-photon microscopy to record from hundreds of GrC axons. Here we show that GrC axon population activity is high dimensional and distributed with little fine-scale spatial structure during spontaneous behaviors. Moreover, distinct behavioral states are represented along orthogonal dimensions in neuronal activity space. These results suggest that the cerebellar cortex supports high-dimensional representations and segregates behavioral state-dependent computations into orthogonal subspaces, as reported in the neocortex. Our findings match the predictions of cerebellar pattern separation theories and suggest that the cerebellum and neocortex use population codes with common features, despite their vastly different circuit structures.