Principles of operation of a cerebellar learning circuit

Herzfeld, David J.; Hall, Nathan J.; Tringides, Marios; Lisberger, Stephen G.

doi:10.7554/elife.55217

Cited by 29 publications

(44 citation statements)

References 89 publications

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“…3 H ). These result are consistent with a recent study using a motor learning paradigm which did not find a correlation between movement speed in the base direction before change in the target direction and learning on the next trial ( Herzfeld et al, 2020 ). Thus, it is unlikely that residual movement on the fixation trials within the fixation window was necessary for learning.…”

Section: Resultssupporting

confidence: 93%

Passive Motor Learning: Oculomotor Adaptation in the Absence of Behavioral Errors

Cain

Botschko

Joshua

2021

eNeuro

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Motor adaptation is commonly thought to be a trial-and-error process in which the accuracy of movement improves with repetition of behavior. We challenged this view by testing whether erroneous movements are necessary for motor adaptation. In the eye movement system, the association between movements and errors can be disentangled, since errors in the predicted stimulus trajectory can be perceived even without movements. We modified a smooth pursuit eye movement adaptation paradigm in which monkeys learn to make an eye movement that predicts an upcoming change in target direction. We trained the monkeys to fixate on a target while covertly, an additional target initially moved in one direction and then changed direction after 250 ms. The monkeys showed a learned response to infrequent probe trials in which they were instructed to follow the moving target. Further experiments confirmed that probing learning or residual eye movements during fixation did not drive learning. These results show that motor adaptation can be elicited in the absence of movement and provide an animal model for studying the implementation of passive motor learning. Current models assume that the interaction between movement and error signals underlies adaptive motor learning. Our results point to other mechanisms that may drive learning in the absence of movement. Significance statement What are the signals that drive learning? Many experimental and theoretical studies have approached this question from the perspective of motor adaptation as it is both extremely relevant to everyday life and allows for tight experimental control. Motor adaptation is thought to be a gradual process in which errors in behavior are corrected. Here we challenged this view and developed a behavioral paradigm for studying whether movement is necessary for motor adaptation. We found that motor adaptive learning can be elicited in the absence of movement, thus suggesting that motor adaptation has a crucial passive component.

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Section: Resultssupporting

confidence: 93%

Passive Motor Learning: Oculomotor Adaptation in the Absence of Behavioral Errors

Cain

Botschko

Joshua

2021

eNeuro

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“…Might it be that interactions between these two areas lead to changes in error sensitivity and reaching biases? While we can only speculate, one possibility might be that prediction errors experienced during passive training cause changes in Purkinje cells (P-cells) in the cerebellar cortex, which rapidly transfer to slower and more robust learning units in the deep cerebellar nucleus (Herzfeld et al, 2020; Lisberger et al, 1994; McCormick & Thompson, 1984; Perret et al, 1993). Thus, interplay between P-cells and the deep nuclei, might be responsible for facilitating the changes in slow state error sensitivity observed early (5 min) after passive training.…”

Section: Discussionmentioning

confidence: 99%

A conversion from slow to fast memory in response to passive motion

Javidialsaadi

Wang

2021

Preprint

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When the same perturbation is experienced consecutively, learning is accelerated on the second attempt. This savings is a central property of sensorimotor adaptation. Current models suggest that these improvements in learning are due to changes in the brain's sensitivity to error. Here, we tested whether these increases in error sensitivity could be facilitated by passive movement experiences. In each experimental group, the robot moved the arm passively in the direction that solved the upcoming rotation, but no visual feedback was provided. Then, following a break in time, participants adapted to a visuomotor rotation. Prior passive movements substantially improved motor learning, increasing total compensation in each group by approximately 30%. Similar to savings, a state-space model suggested that this improvement in learning was due to an increase in error sensitivity, but not memory retention. Thus, passive memories appeared to increase the motor learning system's sensitivity to error. However, some features in the observed behavior were not captured by this model, nor by similar empirical models, which assumed that learning was due a single exponential process. When we considered the possibility that learning was supported by parallel fast and slow adaptive processes, a striking pattern emerged; whereas initial improvements in learning were driven by a slower adaptive state, increases in error sensitivity gradually transferred to a faster learning system with the passage of time.

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“…Both induce plasticity that affects behavior, but the effects are asymmetric: simple spike rates change more after experience of a complex spike (as compared to when the complex spike is missing), and may also decay more with passage of time. In addition, the conjecture that P-cells act as surrogate teachers for their DCN neurons (Medina and Mauk, 1999) raises the possibility that even slower timescales of adaptation arise from plasticity in the DCN neurons (Herzfeld et al, 2020), though this conjecture also remains to be tested. The asymmetric response of a P-cell to presence or absence of a complex spike may be responsible for the fact that reversal of error during extinction training does not reverse the effects of past learning.…”

Section: Discussionmentioning

confidence: 99%

Population coding in the cerebellum: a machine learning perspective

Shadmehr¹

2020

Journal of Neurophysiology

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The cerebellum resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells), and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared to the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus, complex spikes can not only act as a teaching signal for a P-cell, but through complex spike synchrony a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

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Principles of operation of a cerebellar learning circuit

Cited by 29 publications

References 89 publications

Passive Motor Learning: Oculomotor Adaptation in the Absence of Behavioral Errors

Passive Motor Learning: Oculomotor Adaptation in the Absence of Behavioral Errors

A conversion from slow to fast memory in response to passive motion

Population coding in the cerebellum: a machine learning perspective

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