Behavioural learning is mediated by cellular plasticity, such as changes in the strength of synapses at specific sites in neural circuits. The theory of cerebellar motor learning1,2,3 relies on movement errors signalled by climbing-fibre inputs to cause long-term depression of synapses from parallel fibres to Purkinje cells4,5. However, a recent review6 has called into question the widely held view that the climbing-fibre input is an ‘all-or-none’ event. In anaesthetized animals, there is wide variation in the duration of the complex spike (CS) caused in Purkinje cells by a climbing-fibre input7. Furthermore, the amount of plasticity in Purkinje cells is graded according to the duration of electrically controlled bursts in climbing fibres8,9. The duration of bursts depends on the ‘state’ of the inferior olive and therefore may be correlated across climbing fibres8,10. Here we provide a potential functional context for these mechanisms during motor learning in behaving monkeys. The magnitudes of both plasticity and motor learning depend on the duration of the CS responses. Furthermore, the duration of CS responses seems to be a meaningful signal that is correlated across the Purkinje-cell population during motor learning. We suggest that during learning, longer bursts in climbing fibres lead to longer-duration CS responses in Purkinje cells, more calcium entry into Purkinje cells, larger synaptic depression, and stronger learning. The same graded impact of instructive signals for plasticity and learning might occur throughout the nervous system.
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We thank C. Hull, L. Glickfeld and R. Mooney for helpful comments, and M. Joshua and J. Lee for ‘blind’ tests of our measurements of complex-spike duration. Research supported by the Howard Hughes Medical Institute and by the National Eye Institute of the National Institutes of Health under Award Number R01-EY003878. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
The authors declare no competing financial interests.
Extended data figures and tables
a, Arrows show directions of ramp target motions used to assess direction tuning. Red and blue arrows show the on and off directions for simple-spike firing in the example Purkinje cell. b, c, Simple-spike firing rate and CS probability as a function of time during pursuit of vertical target motion in the on direction for simple-spike firing (b, red traces) and the off direction for simple-spike firing (c, blue traces). The target-position traces show the step-ramp target motion, which used a small step in one direction to obviate the need for saccades during the initiation of pursuit in the direction of the target ramp. d, e, Full direction tuning analysis for the simple-spike firing rate (d) and CS probability (e). Each graph comprises eight peri-stimulus time histograms that start 200 ms before the onset of target motion and are positioned appropriately for the direction of target motion. A polar plot appears at the centre of each panel showing the direction tuning as a black line, and the baseline response during fixation as a grey circle. In this example and in all Purkinje cells in our sample, simple-spike firing rate and CS probability had opposite preferred directions.
Extended Data Figure 2 Relationship between number of spikelets in a CS response and the duration of the extracellular potential.
Each graph shows data for one of 16 Purkinje cells with CS responses that were isolated well enough to allow reliable counting of spikelets. Each symbol shows the measurements for a single CS response. The regression lines were obtained with a type-II regression analysis that gave equal weight to errors along the x and y axes. The duration of the CS response to a learning instruction did not vary as a function of tiny fluctuations in the magnitude of the instruction, caused by variation in the eye velocity at the time the instructive target motion occurred. The correlation coefficients for the individual graphs ranged from 0.71 to 0.93 and averaged 0.83. The arrows indicate the time chosen as the end of each CS waveform.
Extended Data Figure 3 Absence of relationship between duration of the post-CS pause in simple-spike firing rate and trial-over-trial depression or learning.
a, An example CS waveform showing the measurement of the duration of the post-CS pause. b, Relationship between duration of pause and duration of CS. Each set of three grey symbols shows data from a single Purkinje cell. Black symbols show averages across the full sample. c, d, Trial-over-trial changes in simple-spike firing rate (c) and eye velocity (d). Red and blue traces show data for long versus short durations of post-CS pause. The light blue shaded area around the blue trace shows one standard error of the mean for the results with a short pause. Vertical dashed lines show the time of the instruction. We did not find any relationship between the magnitude of trial-over-trial depression of simple-spike firing or eye velocity and the duration of the post-CS pause in simple-spike responses. Thus, the post-CS pause might affect learning in the deep cerebellar nucleus30, but does not seem to impact short-term learning in the cerebellar cortex. In terms of the methods, as before, we formed distributions of the duration of the post-CS pause for each Purkinje cell, trisected the distributions, and divided pairs of trials according to whether the post-CS pause in the instruction trial was long, medium or short (means: 42.6, 29.5 and 20.2 ms). The simple-spike activity was somewhat higher on trials with a short versus long post-CS pause (80.4 ± 7.0 versus 73.6 ± 6.9 s.e.m., two-tailed paired t-test, P < 0.01, n = 34).
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Yang, Y., Lisberger, S. Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature 510, 529–532 (2014). https://doi.org/10.1038/nature13282
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