# Coding and processing of error signals in inferior olivary-cerebellar networks

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $396,250

## Abstract

PROJECT SUMMARY
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?”.

## Key facts

- **NIH application ID:** 9852471
- **Project number:** 5R01MH093727-10
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** JAVIER F MEDINA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $396,250
- **Award type:** 5
- **Project period:** 2011-06-02 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9852471

## Citation

> US National Institutes of Health, RePORTER application 9852471, Coding and processing of error signals in inferior olivary-cerebellar networks (5R01MH093727-10). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9852471. Licensed CC0.

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