Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration

Yang, Yan; Lisberger, Stephen G.

doi:10.1038/nature13282

Cited by 208 publications

(221 citation statements)

References 30 publications

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“…Several studies have documented motor learning independent of complex spike activation (Catz et al, 2005;Ke et al, 2009;Dash et al, 2010;Kimpo et al, 2014). Others have documented strong coupling between changes in simple spike activity and movement coupled to climbing fiber activation (Gilbert and Thach, 1977;Medina and Lisberger, 2008;Yang and Lisberger, 2014). Typically, in these studies, simple spike firing decreases, consistent with the Marr-Albus-Ito hypothesis and long-term depression at the parallel fiber-Purkinje cell synapse (Ito, 2001).…”

Section: Simple Spike Changes To Parameter Timingmentioning

confidence: 48%

Changes in Purkinje Cell Simple Spike Encoding of Reach Kinematics during Adaption to a Mechanical Perturbation

2015

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The cerebellum is essential in motor learning. At the cellular level, changes occur in both the simple spike and complex spike firing of Purkinje cells. Because simple spike discharge reflects the main output of the cerebellar cortex, changes in simple spike firing likely reflect the contribution of the cerebellum to the adapted behavior. Therefore, we investigated in Rhesus monkeys how the representation of arm kinematics in Purkinje cell simple spike discharge changed during adaptation to mechanical perturbations of reach movements. Monkeys rapidly adapted to a novel assistive or resistive perturbation along the direction of the reach. Adaptation consisted of matching the amplitude and timing of the perturbation to minimize its effect on the reach. In a majority of Purkinje cells, simple spike firing recorded before and during adaptation demonstrated significant changes in position, velocity, and acceleration sensitivity. The timing of the simple spike representations change within individual cells, including shifts in predictive versus feedback signals. At the population level, feedback-based encoding of position increases early in learning and velocity decreases. Both timing changes reverse later in learning. The complex spike discharge was only weakly modulated by the perturbations, demonstrating that the changes in simple spike firing can be independent of climbing fiber input. In summary, we observed extensive alterations in individual Purkinje cell encoding of reach kinematics, although the movements were nearly identical in the baseline and adapted states. Therefore, adaption to mechanical perturbation of a reaching movement is accompanied by widespread modifications in the simple spike encoding.

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Section: Simple Spike Changes To Parameter Timingmentioning

confidence: 48%

Changes in Purkinje Cell Simple Spike Encoding of Reach Kinematics during Adaption to a Mechanical Perturbation

2015

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“…The component of adaptation expressed at low preparation times appears to resemble implicit, error-driven learning of the kind that appears to be supported by the cerebellum (Medina et al, 2001;Tseng et al, 2007;Taylor et al, 2014;Yang and Lisberger, 2014). Although this component of learning could be expressed at low preparation time, the only long-term memory it exhibited was a weak overall bias of reach direction on Day 2.…”

Section: Declarative Versus Procedural Memorymentioning

confidence: 99%

The Influence of Movement Preparation Time on the Expression of Visuomotor Learning and Savings

Haith¹,

Huberdeau²,

2015

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Following a change in the environment or motor apparatus, human subjects are able to rapidly compensate their movements to recover accurate performance. This ability to adapt is thought to be achieved through multiple, qualitatively distinct learning processes acting in parallel. It is unclear, however, what the relative contributions of these multiple processes are during learning. In particular, long-term memories in such paradigms have been extensively studied through the phenomenon of savings-faster adaptation to a given perturbation the second time it is experienced-but it is unclear which components of learning contribute to this effect. Here we show that distinct components of learning in an adaptation task can be dissociated based on the amount of preparation time they require. During adaptation, we occasionally forced subjects to generate movements at very low preparation times. Early in learning, subjects expressed only a limited amount of their prior learning in these trials, though performance improved gradually with further practice. Following washout, subjects exhibited a strong and persistent aftereffect in trials in which preparation time was limited. When subjects were exposed to the same perturbation twice in successive days, they adapted faster the second time. This savings effect was, however, not seen in movements generated at low preparation times. These results demonstrate that preparation time plays a critical role in the expression of some components of learning but not others. Savings is restricted to those components that require prolonged preparation to be expressed and might therefore reflect a declarative rather than procedural form of memory.

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“…We report data from experiments on six awake, behaving adult male rhesus monkeys. Four of the monkeys were used at the University of California, San Francisco (UCSF) for recordings from Purkinje cells in the floccular complex during pursuit learning; two of these also provided recordings from Purkinje cells for other papers (Yang and Lisberger, 2013;Yang and Lisberger, 2014) and two provided data used by Medina andLisberger (2008, 2009). Two of the monkeys were used at Duke University for behavioral studies of pursuit learning.…”

Section: Methodsmentioning

confidence: 99%

“…Learning is large when the instruction evokes a CS response, and quite small when it does not. We have argued before for the CS-frequent Purkinje cells that the link from the presence of a CS on the instruction trial to much larger trialover-trial learning in the behavior is caused by correlations in the CS responses across the Purkinje cell population (Yang and Lisberger, 2013;Yang and Lisberger, 2014). For the CSinfrequent Purkinje cells, the trial-over-trial learning in eye velocity is independent of the occurrence of a CS on the instruction trial (Fig.…”

Section: Basis For Different Learning In Cs-frequent Versus Csinfrequmentioning

confidence: 99%

Role of Plasticity at Different Sites across the Time Course of Cerebellar Motor Learning

Yang

Lisberger

2014

J. Neurosci.

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Learning comprises multiple components that probably involve cellular and synaptic plasticity at multiple sites. Different neural sites may play their largest roles at different times during behavioral learning. We have used motor learning in smooth pursuit eye movements of monkeys to determine how and when different components of learning occur in a known cerebellar circuit. The earliest learning occurs when one climbing-fiber response to a learning instruction causes simple-spike firing rate of Purkinje cells in the floccular complex of the cerebellum to be depressed transiently at the time of the instruction on the next trial. Trial-over-trial depression and the associated learning in eye movement are forgotten in Ͻ6 s, but facilitate long-term behavioral learning over a time scale of ϳ5 min. During 100 repetitions of a learning instruction, simple-spike firing rate becomes progressively depressed in Purkinje cells that receive climbingfiber inputs from the instruction. In Purkinje cells that prefer the opposite direction of pursuit and therefore do not receive climbing-fiber inputs related to the instruction, simple-spike responses undergo potentiation, but more weakly and more slowly. Analysis of the relationship between the learned changes in simple-spike firing and learning in eye velocity suggests an orderly progression of plasticity: first on Purkinje cells with complex-spike (CS) responses to the instruction, later on Purkinje cells with CS responses to the opposite direction of instruction, and last in sites outside the cerebellar cortex. Climbing-fiber inputs appear to play a fast and primary, but nonexclusive, role in pursuit learning.

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Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration

Cited by 208 publications

References 30 publications

Changes in Purkinje Cell Simple Spike Encoding of Reach Kinematics during Adaption to a Mechanical Perturbation

Changes in Purkinje Cell Simple Spike Encoding of Reach Kinematics during Adaption to a Mechanical Perturbation

The Influence of Movement Preparation Time on the Expression of Visuomotor Learning and Savings

Role of Plasticity at Different Sites across the Time Course of Cerebellar Motor Learning

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