scholarly journals Two functionally distinct Purkinje cell populations implement an internal model within a single olivo-cerebellar loop

2021 ◽  
Author(s):  
Dora Angelaki ◽  
Jean Laurens
Keyword(s):  
Purkinje Cells ◽  
Prediction Error ◽  
Internal Model ◽  
Inferior Olive ◽  
Cell Populations ◽  
Otolith Organs ◽  
Prediction Errors ◽  
Spike Response ◽  
Sensory Prediction ◽  
Two Populations

Olivo-cerebellar loops, where anatomical patches of the cerebellar cortex and inferior olive project one onto the other, form an anatomical unit of cerebellar computation. Here, we investigated how successive computational steps map onto olivo-cerebellar loops. Lobules IX-X of the cerebellar vermis, i.e. the nodulus and uvula, implement an internal model of the inner ear's graviceptor, the otolith organs. We have previously identified two populations of Purkinje cells that participate in this computation: Tilt-selective cells transform egocentric rotation signals into allocentric tilt velocity signals, to track head motion relative to gravity, and translation-selective cells encode otolith prediction error. Here we show that, despite very distinct simple spike response properties, both types of Purkinje cells emit complex spikes that are proportional to sensory prediction error. This indicates that both cell populations comprise a single olivo-cerebellar loop, in which only translation-selective cells project to the inferior olive. We propose a neural network model where sensory prediction errors computed by translation-selective cells are used as a teaching signal for both populations, and demonstrate that this network can learn to implement an internal model of the otoliths.

scholarly journals The cerebellum does more than sensory-prediction-error-based learning in sensorimotor adaptation tasks

2017 ◽  
Author(s):  
Peter A. Butcher ◽  
Richard B. Ivry ◽  
Sheng-Han Kuo ◽  
David Rydz ◽  
John W. Krakauer ◽  
...  
Keyword(s):  
Implicit Learning ◽  
Prediction Error ◽  
Internal Model ◽  
Sensorimotor Adaptation ◽  
Prediction Errors ◽  
Cerebellar Dysfunction ◽  
Visuomotor Rotation ◽  
Cerebellar Damage ◽  
Internal Forward Model ◽  
Sensory Prediction

AbstractIndividuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory-prediction-error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why don’t individuals with cerebellar damage come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, prior to executing a movement on each trial, the participants verbally reported their intended aiming location. Compared to healthy controls, participants with spinocerebellar ataxia (SCA) displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly (Exp. 1) or gradually (Exp. 2). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability (Exp. 3). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory-prediction-error-based learning, but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials.New and noteworthyIndividuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory prediction errors to update an internal model. Here, we show that these individuals also have difficulty in discovering an aiming solution to overcome their adaptation deficit, suggesting a new role for the cerebellum in sensorimotor adaptation tasks.

scholarly journals Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

Brain ◽  
2019 ◽  
Vol 142 (3) ◽  
pp. 662-673 ◽  
Author(s):  
Aaron L Wong ◽  
Cherie L Marvel ◽  
Jordan A Taylor ◽  
John W Krakauer
Keyword(s):  
Prediction Error ◽  
Poor Performance ◽  
Visuomotor Adaptation ◽  
Cerebellar Degeneration ◽  
Prediction Errors ◽  
Cerebellar Disease ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Age Related ◽  
Sensory Prediction

Abstract Systematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed—by allowing vision of the hand, which eliminates sensory-prediction errors—patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction error signal to zero. We demonstrated in younger healthy control subjects that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with cerebellar degeneration could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed (although they could not improve beyond what they retrieved), and retain it for at least 1 year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age 18–33 years), likely due to age-related reductions in spatial and working memory. Patients also failed to generalize, i.e. they were unable to adopt analogous aiming strategies in response to novel rotations. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction error-based learning—even when this system is impaired in patients with cerebellar disease. The persistence of sensory-prediction error-based learning effectively suppresses a switch to target error-based learning, which perhaps explains the unexpectedly poor performance by patients with cerebellar degeneration in visuomotor adaptation tasks.

scholarly journals Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

2018 ◽  
Author(s):  
Aaron L. Wong ◽  
Cherie L. Marvel ◽  
Jordan A. Taylor ◽  
John W. Krakauer
Keyword(s):  
Spinocerebellar Ataxia ◽  
Prediction Error ◽  
Visuomotor Adaptation ◽  
Cerebellar Degeneration ◽  
Prediction Errors ◽  
Healthy Controls ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Age Related ◽  
Sensory Prediction

ABSTRACTSystematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction-error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed – by allowing vision of the hand, which eliminates sensory-prediction errors – patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction-error signal to zero. We demonstrated in younger healthy controls that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with spinocerebellar ataxia could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed, and retain it for at least one year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age, 18-33), likely due to age-related reductions in spatial and working memory. Moreover, patients failed to generalize, i.e., they were unable to adopt analogous aiming strategies in response to novel rotations, nor could they further improve their performance without vision of the hand. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction-error-based learning – even when this system is impaired in patients with cerebellar degeneration. The persistence of sensory-prediction-error-based learning effectively suppresses a switch to target-error-based learning, which perhaps explains the unexpectedly poor performance by patients with spinocerebellar ataxia in visuomotor adaptation tasks.

scholarly journals Task Errors Do Not Induce Implicit Sensorimotor Learning

2020 ◽  
Author(s):  
Anushka Oza ◽  
Adarsh Kumar ◽  
Pratik K. Mutha
Keyword(s):  
Implicit Learning ◽  
Prediction Error ◽  
Cognitive Process ◽  
Target Location ◽  
Prediction Errors ◽  
Verbal Instruction ◽  
Reaching Task ◽  
Task Success ◽  
Task Goals ◽  
Sensory Prediction

ABSTRACTHumans implicitly adjust their movements when challenged with perturbations that induce sensory prediction errors. Recent work suggests that failure to accomplish task goals could function as a gain on this prediction-error-driven adaptation or could independently trigger additional implicit mechanisms to bring about greater net learning. We aimed to distinguish between these possibilities using a reaching task wherein prediction errors were fixed at zero, but task success was modulated via changes in target location and size. We first observed that task failure caused changes in hand angle that showed classic signatures of implicit learning. Surprisingly however, these adjustments were eliminated when participants were explicitly instructed to ignore task errors. These results fail to support the idea that task errors independently induce implicit learning, and instead endorse the view that they provide a distinct signal to an intentional cognitive process that is responsive to verbal instruction.

scholarly journals Does somatosensory acuity influence the extent of internal model recalibration in young and older adults?

2020 ◽  
Author(s):  
Koenraad Vandevoorde ◽  
Jean-Jacques Orban de Xivry
Keyword(s):  
Older Adults ◽  
Sensory Integration ◽  
Prediction Error ◽  
Internal Model ◽  
Sensory Feedback ◽  
Motor Adaptation ◽  
Age Related ◽  
Somatosensory Information ◽  
Implicit Motor ◽  
Sensory Prediction

AbstractThe ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, automatic adjustment of movement, or internal model recalibration, remains intact and might even be increased with aging. Since somatosensory information appears to be required for internal model recalibration, it might well be that an age-related decline in somatosensory acuity is linked to the increase of internal model recalibration. One possible explanation for an increased internal model recalibration is that age-related somatosensory deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced somatosensory acuity results in an increased reliance on predicted sensory feedback. Both these explanations led to our preregistered hypothesis: we expect a relation between the decline of somatosensation and the increased internal model recalibration with aging. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and somatosensory signals during motor adaptation.New & NoteworthyIs somatosensory acuity linked to implicit motor adaptation? The latter is larger in old compared to younger people? In light of reliability-based sensory integration, we hypothesized that this larger implicit adaptation was linked to an age-related lower reliability of somatosensation. Over two experiments and 130 participants, we failed to find any evidence for this. We discuss alternative explanations for the increase in implicit adaptation with age and the validity of our somatosensory assessment.

scholarly journals Dopamine neuron ensembles signal the content of sensory prediction errors

2019 ◽  
Author(s):  
Thomas A. Stalnaker ◽  
James D. Howard ◽  
Yuji K. Takahashi ◽  
Samuel J. Gershman ◽  
Thorsten Kahnt ◽  
...  
Keyword(s):  
Neural Activity ◽  
Prediction Error ◽  
Sensory Information ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Bold Response ◽  
Dopamine System ◽  
Sensory Event ◽  
Specific Teaching ◽  
Sensory Prediction

AbstractDopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in individual neuron or average neural activity. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

scholarly journals Rethinking dopamine as generalized prediction error

2017 ◽  
Author(s):  
Matthew P.H. Gardner ◽  
Geoffrey Schoenbaum ◽  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Sensory Prediction ◽  
Lines Of Evidence ◽  
Midbrain Dopamine Neurons

AbstractMidbrain dopamine neurons are commonly thought to report a reward prediction error, as hypothesized by reinforcement learning theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signaling errors in both sensory and reward predictions, dopamine supports a form of reinforcement learning that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and reward prediction errors, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.

scholarly journals Simple spike dynamics of Purkinje cells in the macaque vestibulo-cerebellum reflect sensory prediction error

2019 ◽  
Author(s):  
Jean Laurens ◽  
Dora E. Angelaki
Keyword(s):  
Prediction Error ◽  
Temporal Integration ◽  
Linear Acceleration ◽  
Cell Types ◽  
Otolith Organs ◽  
Response Dynamics ◽  
On Line ◽  
Motor Actions ◽  
Short Time ◽  
Sensory Prediction

AbstractTheories of cerebellar functions posit that the cerebellum implements forward models for online correction of motor actions and sensory estimation. As an example of such computations, a forward model compensates for a sensory ambiguity where the peripheral otolith organs in the inner ear sense both head tilts and translations. Here we exploit the response dynamics of two functionally-coupled Purkinje cell types in the caudal vermis to understand their role in this computation. We find that one population encodes tilt velocity, whereas the other, translation-selective, population encodes linear acceleration. Using a dynamical model, we further show that these signals likely represent sensory prediction error for the on-line updating of tilt and translation estimates. These properties also reveal the need for temporal integration between the tilt-selective velocity and translation-selective acceleration population signals. We show that a simple model incorporating a biologically plausible short time constant can mediate the required temporal integration.

scholarly journals Rethinking dopamine as generalized prediction error

2018 ◽  
Vol 285 (1891) ◽  
pp. 20181645 ◽  
Author(s):  
Matthew P. H. Gardner ◽  
Geoffrey Schoenbaum ◽  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Sensory Prediction ◽  
Lines Of Evidence ◽  
Midbrain Dopamine Neurons

Midbrain dopamine neurons are commonly thought to report a reward prediction error (RPE), as hypothesized by reinforcement learning (RL) theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here, we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signalling errors in both sensory and reward predictions, dopamine supports a form of RL that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and RPEs, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.

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