The cerebellum plays a key role in motor control, particularly in motor learning. More recently, the cerebellum has also been implicated in cognitive processing. Indeed, cerebellar damage is associated with a wide range of neurological and neuropsychatric disorders, including ataxia, dystonia, schizophrenia and autism. Despite this apparent functional heterogeneity, the cerebellar microcircuit is remarkably homogeneous, both across its different regions and across different animal species. Thus, it has been suggested that the cerebellum may accomplish its job by performing one universal computation, and then sending out the results of the computation to other brain areas, both motor and non-motor. Previous work indicates that the universal computation performed by the cerebellum involves using past experience to predict future events, including those that are caused by our own movements. The long-term objective of this project is to achieve a full mechanistic understanding of how the cerebellum learns to make these predictions. The focus will be on the error signals that are critical for alerting the cerebellum that a prediction was wrong and needs to be updated. In the three aims of this proposal, we examine: 1) positive prediction errors (when something unexpected happens), 2) negative prediction errors (when something expected doesn't happen), and 3) temporal- difference prediction errors (when a stimulus predicts that something is about to happen). All experiments are done on a newly developed treadmill apparatus for eyeblink conditioning in head-fixed mice. Eyeblink conditioning was chosen as the model system because it offers a number of advantages for the project: 1) The behaviorally-relevant error signals are under experimental control and can be easily manipulated, 2) The basic conditioning task can be modified to ask questions about the role of error signals in driving both motor learning and higher-order associations, and 3) The olivo-cerebellar regions that are critical for processing error signals have been identified. By combining the elegant simplicity of eyeblink conditioning with new technologies for optogenetics, electrophysiology, and two-photon calcium imaging, the proposed experiments will record and manipulate the error-related neural signals present during the learning process with an unprecedented level of temporal and cellular specificity. This research could help develop new therapeutic approaches to treat motor and cognitive disorders associated with cerebellar dysfunction, not by targeting molecular mechanisms of neural plasticity, but the instructive error-related signals that drive them. In this regard, the specific aims of the application are designed to ask not only ?what is the neural code for error signals in the cerebellum?, but also ?how can we manipulate the code to enhance learning??.

Public Health Relevance

Making errors is typically considered a nuisance, but in fact, error information is essential for driving adaptation, improving performance, and updating expectations about future events in an uncertain and ever-changing world. The goal of this proposal is to understand the way that olivo-cerebellar circuits in the brain encode and process this all-important error information. This research aims to uncover fundamental principles of learning that are particularly relevant for neurological disorders associated with cerebellar dysfunction, like ataxia and dystonia, and for other neuropsychiatric disorders like ADHD, autism, and schizophrenia, whose symptoms have been linked to impairments in the ability to monitor action outcomes and recognize errors as such.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH093727-10
Application #
9852471
Study Section
Sensorimotor Integration Study Section (SMI)
Program Officer
Buhring, Bettina D
Project Start
2011-06-02
Project End
2021-12-31
Budget Start
2020-01-01
Budget End
2020-12-31
Support Year
10
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Baylor College of Medicine
Department
Neurosciences
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Raymond, Jennifer L; Medina, Javier F (2018) Computational Principles of Supervised Learning in the Cerebellum. Annu Rev Neurosci 41:233-253
Giovannucci, Andrea; Badura, Aleksandra; Deverett, Ben et al. (2017) Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nat Neurosci 20:727-734
Ten Brinke, Michiel M; Heiney, Shane A; Wang, Xiaolu et al. (2017) Dynamic modulation of activity in cerebellar nuclei neurons during pavlovian eyeblink conditioning in mice. Elife 6:
Yang, Yue; Yamada, Tomoko; Hill, Kelly K et al. (2016) Chromatin remodeling inactivates activity genes and regulates neural coding. Science 353:300-305
Johansson, Fredrik; Hesslow, Germund; Medina, Javier F (2016) Mechanisms for motor timing in the cerebellar cortex. Curr Opin Behav Sci 8:53-59
Ohmae, Shogo; Medina, Javier F (2015) Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice. Nat Neurosci 18:1798-803
Lisberger, Stephen G; Medina, Javier F (2015) How and why neural and motor variation are related. Curr Opin Neurobiol 33:110-6
Najafi, Farzaneh; Giovannucci, Andrea; Wang, Samuel S-H et al. (2014) Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice. Elife 3:e03663
Heiney, Shane A; Wohl, Margot P; Chettih, Selmaan N et al. (2014) Cerebellar-dependent expression of motor learning during eyeblink conditioning in head-fixed mice. J Neurosci 34:14845-53
Najafi, Farzaneh; Giovannucci, Andrea; Wang, Samuel S-H et al. (2014) Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice. Cell Rep 6:792-798

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