Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Mar 30;9:e54073.
doi: 10.7554/eLife.54073.

Prediction Signals in the Cerebellum: Beyond Supervised Motor Learning

Free PMC article

Prediction Signals in the Cerebellum: Beyond Supervised Motor Learning

Court Hull. Elife. .
Free PMC article


While classical views of cerebellar learning have suggested that this structure predominantly operates according to an error-based supervised learning rule to refine movements, emerging evidence suggests that the cerebellum may also harness a wider range of learning rules to contribute to a variety of behaviors, including cognitive processes. Together, such evidence points to a broad role for cerebellar circuits in generating and testing predictions about movement, reward, and other non-motor operations. However, this expanded view of cerebellar processing also raises many new questions about how such apparent diversity of function arises from a structure with striking homogeneity. Hence, this review will highlight both current evidence for predictive cerebellar circuit function that extends beyond the classical view of error-driven supervised learning, as well as open questions that must be addressed to unify our understanding cerebellar circuit function.

Keywords: cerebellum; motor learning; neural circuits; neuroscience.

Conflict of interest statement

CH No competing interests declared


Figure 1.
Figure 1.. Circuit diagram of cerebellar input and output pathways.
Climbing fibers originate in the inferior olive (IO), and excite (plus signs) Purkinje cell dendrites. These inputs serve to instruct heterosynaptic plasticity at synapses from the mossy fiber pathway, an excitatory pathway that originates in the pontine nuclei and elsewhere. The mossy fiber pathway excites granule cells, and terminates in excitatory parallel fiber inputs onto Purkinje cells. Purkinje cells are inhibitory (minus sign), and regulate the activity of output neurons in the cerebellar nuclei.
Figure 2.
Figure 2.. CF input to Purkinje cells obeys the principles of TD learning.
CF-driven Cspks, recorded here after learning, are more probable in response to a US (corneal airpuff) that is unexpected (black) than when the same stimulus is expected (red). Cspks also follow the conditioned stimulus after learning, and are reduced below baseline levels when the expected US does not occur.
Figure 3.
Figure 3.. Cspk activity reflects predictions about reward delivery.
Left, mice were trained to press and release a lever as instructed by a visual cue in order to receive reward. Middle, Cspk activity was greatest for lever releases that predicted reward delivery. Right, Cspk activity was enhanced when expected reward was not delivered.
Figure 4.
Figure 4.. Schematic of CF activity in a TD learning framework.
Before learning, CFs are driven by an unexpected US. After learning, CFs are driven by a CS that accurately predicts the US (CS1). If a new CS occurs earlier in time (CS2), CF activity can then be driven by this higher order stimulus. Finally, if the expected US is omitted, CF activity is reduced (negative prediction error), serving to extinguish associations with the no longer appropriate CS1 and CS2.
Figure 5.
Figure 5.. Granule cells develop reward-predictive activity in both operant and Pavlovian conditioning tasks.
Left, population level calcium imaging in mice reveals distinct populations of granule cells that represent reward delivery (top), reward omission (middle), and the anticipation of reward delivery (bottom) after learning in an operant forelimb task. Right, the same categories of responses arise following learning in a Pavlovian task where a neutral cue (CS) is associated with reward.
Figure 6.
Figure 6.. LTD is thought to provide a key mechanism for reducing Purkinje cell output to enable learning.
Parallel fibers (Figure 1) in the mossy fiber pathway carrying CS input are thought to be depressed when paired with CF input to Purkinje cells. This enables a well-timed reduction in Purkinje cell inhibition of CbN cells. LTP of mossy fiber input to CbN cells may also facilitate learning (Pugh and Raman, 2006).
Figure 7.
Figure 7.. Cspk activity is correlated with both the depression of Purkinje cell simple spiking and learning on a trial-by-trial basis.
Left, Cspks on the preceding trial lead to a depression of Purkinje cell simple spiking on the next trial that is proportional to the duration of the Cspk. Right, trial over trial changes in eye velocity obey the same relationship to Cspk duration as the depression in simple spiking.
Figure 8.
Figure 8.. CbN neuron spiking is proportional to mismatch between actual and predicted head velocity.
Top, head velocity traces for a monkey making voluntary and involuntary (imposed via rotational turntable) head movements. When force is applied from an external motor (gray interval), the monkey must slowly adapt its head movement to account for the oppositional force and recover normal head velocity. Catch trials are interleaved where no motor-restriction is applied. Middle, a representative CbN neuron fires only when head movement differs from expectation, and its firing rate is directly proportional to the difference between actual and expected head movement across time.
Figure 9.
Figure 9.. Inhibiting cerebellar CbN output abolishes ramping activity in ALM.
Left, inhibition of the CbN (fastigial) prevents ALM ramping in a whisker-based sensory discrimination task. Right, inhibition of the CbN (dentate) prevents ALM ramping in a virtual reality conditioning task. Modified from Gao et al. (2018) (Left) and Chabrol et al. (2019) (Right) with permission from N Li, K Svoboda, CI DeZeeuw (left) and T Mrsic-Flogel (right).
Figure 10.
Figure 10.. CbN projections to the VTA signal during a social behavioral context.
Top, behavioral chamber where a test mouse can explore either a novel object (green area) or novel animal (yellow area). Fiber photometry was used to measure the activity of VTA projecting CbN neurons. Bottom, VTA projecting CbN neurons are preferentially active when the test mouse explores the novel animal.

Similar articles

See all similar articles


    1. Ackermann H. Cerebellar contributions to speech production and speech perception: psycholinguistic and neurobiological perspectives. Trends in Neurosciences. 2008;31:265–272. doi: 10.1016/j.tins.2008.02.011. - DOI - PubMed
    1. Albergaria C, Silva NT, Pritchett DL, Carey MR. Locomotor activity modulates associative learning in mouse cerebellum. Nature Neuroscience. 2018;21:725–735. doi: 10.1038/s41593-018-0129-x. - DOI - PMC - PubMed
    1. Albus JS. A theory of cerebellar function. Mathematical Biosciences. 1971;10:25–61. doi: 10.1016/0025-5564(71)90051-4. - DOI
    1. Apps R. Movement-related gating of climbing fibre input to cerebellar cortical zones. Progress in Neurobiology. 1999;57:537–562. doi: 10.1016/S0301-0082(98)00068-9. - DOI - PubMed
    1. Apps R, Garwicz M. Anatomical and physiological foundations of cerebellar information processing. Nature Reviews Neuroscience. 2005;6:297–311. doi: 10.1038/nrn1646. - DOI - PubMed