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Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion

Abstract

There is considerable evidence that the cerebellum has a vital role in motor learning by constructing an estimate of the sensory consequences of movement. Theory suggests that this estimate is compared with the actual feedback to compute the sensory prediction error. However, direct proof for the existence of this comparison is lacking. We carried out a trial-by-trial analysis of cerebellar neurons during the execution and adaptation of voluntary head movements and found that neuronal sensitivities dynamically tracked the comparison of predictive and feedback signals. When the relationship between the motor command and resultant movement was altered, neurons robustly responded to sensory input as if the movement was externally generated. Neuronal sensitivities then declined with the same time course as the concurrent behavioral learning. These findings demonstrate the output of an elegant computation in which rapid updating of an internal model enables the motor system to learn to expect unexpected sensory inputs.

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Figure 1: Experimental design.
Figure 2: Learning procedure.
Figure 3: Average head velocity and sensitivity for our population of rFN neurons (n = 21) during the learning phase.
Figure 4: Extinction phase.
Figure 5: Average head velocity and sensitivity for our population of rFN neurons (n = 21) during the extinction phase.
Figure 6: Activity of an example neuron recorded in the VN.
Figure 7: Average head velocity and neuronal sensitivity for our population of neurons recorded in the VN (n = 20).

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Acknowledgements

We thank M. Chacron, M. Jamali, D. Mitchell, A. Dale and I. Mackrous for helpful discussions and critical reading of the manuscript, W. Kucharski for mechanical expertise and S. Nuara for animal care assistance. This study was supported by grants from the Canadian Institute of Health Research (MOP-42440), US National Institutes of Health (R01 DC002390) and from the Fonds Québécois de la Recherche sur la Nature et les Technologies to J.X.B.

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Authors

Contributions

J.X.B., J.C. and K.E.C. designed the study. J.X.B. and J.C. performed the experiments. J.X.B. and J.C. analyzed the data. K.E.C., J.X.B. and J.C. wrote the paper.

Corresponding author

Correspondence to Kathleen E Cullen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 A typical bimodal neuron is unresponsive during control active head-on-body motion, and remains so during learning and catch trials.

A. Top row shows the head velocity during control trials, learning phase and catch trials overlaying a minimum of 5 trials. Bottom row shows the firing rates corresponding to the head movements above. Grey lines show individual trials and black lines show the average responses. B. The magnitude of head velocity error during control, learning, and catch trials. During the learning phase, the monkey initially made slower head movements as quantified by an increase in head velocity error. As learning further progressed, head velocity increased nearing control values as indicated by the striking decrease in head velocity error magnitude (light blue bars). C. Normalized sensitivity to corresponding head movements shown above. During the learning phase, the neuron remained insensitive as predicted by its response to active head-on-body motion before learning (light bluebars). Similarly, the minimal response for catch trials (red), was comparable to that measured for active head on body movements in the control condition and during learning. Data in B and C show average and error bars represent ±SEM.

Supplementary Figure 2 Average head velocity and sensitivity for our population of bimodal neurons during the learning phase.

A. Normalized head velocity for control trials before learning, learning phase and catch trials. B. Normalized neuronal sensitivity for control trials, the learning phase and catch trials. Data in A and B show average and error bars represent ±SEM. C. Scatter plot of peak head velocity errors over time for each trial during the learning phase. D. Scatter plot of normalized neuronal sensitivity over time for each trial during the learning phase. Black lines show exponential fits to the data.

Supplementary Figure 3 Trial-by trial analysis of single rFN neuron responses.

A,B. Exponential fits to the head movement error (A) and neuronal sensitivity data (B) for each cell collected during learning (Grey lines). C,D. exponential fits to the head movement error and neuronal sensitivity data collected for each cell collected during learning extinction. Finally, for each panel, the data for the individual example cell is superimposed (blue circles).

Supplementary Figure 4 A typical bimodal neuron remains unresponsive during the extinction of learning.

A. Top row shows the head velocity during the extinction phase overlaying a minimum of 5 trials. Bottom row shows the firing rates corresponding to the head movements above. Grey lines show individual trials and black lines show the average. B. Magnitude of head velocity error during the extinction phase. C. Normalized neuronal sensitivity to head motion during the extinction phase. Data in B and C show average and error bars represent ±SEM. Data from the control (before learning) and catch trials are reproduced here for comparison.

Supplementary Figure 5 Average head velocity and sensitivity for our population of bimodal neurons during the extinction phase.

A. Normalized head velocity for control trials before learning, catch trials and extinction phase. B. Normalized neuronal sensitivity for control trials before learning, catch trials and extinction phase. Data in A and B show average and error bars represent ±SEM. C. Scatter plot of peak head velocity error over time for each trial during the extinction phase. D. Scatter plot of normalized sensitivity over time for each trial during the extinction phase. Black lines show exponential fits to the data.

Supplementary Figure 6 Trial-by trial analysis of single VN neuron responses.

A,B. Exponential fits to the head movement error (A) and neuronal sensitivity data (B) for each cell during learning (Grey lines). C,D. exponential fits to the head movement error and neuronal sensitivity data for each cell collected during learning extinction. Finally, for each panel, the data for the individual example cell is superimposed (blue circles).

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Brooks, J., Carriot, J. & Cullen, K. Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion. Nat Neurosci 18, 1310–1317 (2015). https://doi.org/10.1038/nn.4077

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