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

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2024 · $574,163

## Abstract

PROJECT SUMMARY
The cerebellum plays a key role in motor control, particularly in motor learning. However, there is increasing
recognition that the cerebellum also participates in cognitive functions such as planning, set-shifting, abstract
reasoning, attention and working memory. Indeed, cerebellar damage is associated with both motor and
cognitive problems and has been linked to a wide range of neurological and neuropsychiatric disorders, from
ataxia and dystonia to schizophrenia and autism. Despite this remarkable functional heterogeneity, the
cerebellar microcircuit is remarkably homogeneous, both across its different regions and across different
animal species. This has led to the theory that the cerebellum may accomplish its job by performing one
universal computation, which can be applied to both motor and non-motor functional domains by connecting
the cerebellum to different brain areas. Much of what we know about this universal computation comes from
mechanistic studies that have revealed what the cerebellum does – and how it does it – by manipulating and
recording neural activity in the cerebellum of mice and other animals performing simple sensorimotor learning
tasks. Based on this previous work, a picture is emerging that the cerebellum is a ‘machine’ specialized for
supervised learning, whose function can be summarized succinctly: to learn how to eliminate errors. While
there is good support for this idea for learning tasks in which the error signals are low-level and conveyed
directly from the sensory periphery to the cerebellum via subcortical pathways, there is almost nothing known
about the function of high-level error signals originating in parts of the brain that are not directly connected to
the sensory or motor periphery. The goal of this project is to start filling this gap by examining how the
cerebellum operates when mice are trained in 3 new learning tasks recently developed, in which the error
signals originate in the primary somatosensory cortex (aim 1), the cerebellum itself (aim 2) and the prefrontal
cortex (aim 3). To assess function, the proposed experiments will record and manipulate the error signals
present during the learning process with an unprecedented level of temporal and cellular specificity, using new
high-density Neuropixels tools and cell-specific photostimulation with optogenetics. This research could help
develop new therapeutic approaches to treat both motor as well as cognitive disorders associated with
deficient connectivity between the cerebellum and the rest of the brain, not by targeting molecular mechanisms
of neural plasticity, but the instructive error-related signals that drive them.

## Key facts

- **NIH application ID:** 10864985
- **Project number:** 5R01MH093727-14
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** JAVIER F MEDINA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $574,163
- **Award type:** 5
- **Project period:** 2011-06-02 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10864985

## Citation

> US National Institutes of Health, RePORTER application 10864985, Coding and processing of error signals in inferior olivary-cerebellar networks (5R01MH093727-14). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10864985. Licensed CC0.

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