Cerebellar granule cells encode the expectation of reward

By M. Wagner, Tony Hyun Kim, J. Savall, Mark J. Schnitzer, Liqun Luo. 2017

460 citations recorded by the source corpus.

The human brain contains approximately 60 billion cerebellar granule cells, which outnumber all other brain neurons combined. Classical theories posit that a large, diverse population of granule cells allows for highly detailed representations of sensorimotor context, enabling downstream Purkinje cells to sense fine contextual changes. Although evidence suggests a role for the cerebellum in cognition, granule cells are known to encode only sensory and motor context. Here, using two-photon calcium imaging in behaving mice, we show that granule cells convey information about the expectation of reward. Mice initiated voluntary forelimb movements for delayed sugar-water reward. Some granule cells responded preferentially to reward or reward omission, whereas others selectively encoded reward anticipation. Reward responses were not restricted to forelimb movement, as a Pavlovian task evoked similar responses. Compared to predictable rewards, unexpected rewards elicited markedly different granule cell activity despite identical stimuli and licking responses. In both tasks, reward signals were widespread throughout multiple cerebellar lobules. Tracking the same granule cells over several days of learning revealed that cells with reward-anticipating responses emerged from those that responded at the start of learning to reward delivery, whereas reward-omission responses grew stronger as learning progressed. The discovery of predictive, non-sensorimotor encoding in granule cells is a major departure from the current understanding of these neurons and markedly enriches the contextual information available to postsynaptic Purkinje cells, with important implications for cognitive processing in the cerebellum.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Liron Sheintuch
2. Alon Rubin
3. Noa Brande-Eilat
4. Nitzan Geva
5. Noa Sadeh
6. Or Pinchasof
7. Yaniv Ziv

Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “Several groups have recently overcome the technical hurdles of recording granule cell population activity by using two-photon imaging and genetically encoded calcium indicators (Giovannucci et al., 2017; Knogler et al., 2017; Wagner et al., 2017; see also Ozden et al., 2012).”
• Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, unknown): “We also placed a cranial window over cerebellar lobules VI, simplex, and crus I (Wagner et al., 2017), regions that are forelimb-related and receive heavy inputs from the pontine nuclei (Huang et al.”
• Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data (2017, result): “…of the different sessions revealed that different subsets of cells were detected in each of the imaging sessions (Figures 4I and 4J), demonstrating that there was no one-to-one mapping of cell identity across sessions, consistent with previous reports (Driscoll et al., 2017; Wagner et al., 2017).”