# Canonical computations for motor learning by the cerebellar cortex micro-circuit

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $1,275,271

## Abstract

Abstract
The cerebellum is critical for learning and executing coordinated, well-timed movements. The cerebellar
cortex seems to have a particular role in learning to time movements. Since the 1960's and 70's, we have
known the architecture of the cerebellar microcircuit, but most analyses of cerebellar function during behavior
have focused on Purkinje cells. Here, we propose to investigate the cerebellar cortex at an entirely new level
by asking how the full cerebellar microcircuit – mossy fiber, granule cells, Golgi cells, molecular layer
interneurons, and Purkinje cells – performs neural computations during motor behavior and motor learning.
We strive to “crack” the circuit by identifying all elements, recording their electrical activity during movement
and learning, and reconstructing a neural circuit model that reproduces the biological data. We will use three
established learning systems that all can learn predictive timing: classical conditioning of the eyelid response
(mice), predictive timing of forelimb movements (mice), and direction learning in smooth pursuit eye
movements (monkeys). Our proposal has six key features. First, optogenetics (in mice) will link the discharge
of different cerebellar interneurons during movement and learning to their molecular cell types. Second, a
machine-learning clustering analysis (in mice and monkeys) will find analogies among the cell populations
recorded in our three preparations and will classify neurons according to their putative cell types based on
recordings of many parameters of non-Purkinje cells during movement and motor learning. Third, multi-
contact electrodes will allow us to record simultaneously from multiple neighboring single neurons and
compute spike-timing cross-correlograms (CCGs) to identify the sign of connections; we also will look for
changes in CCGs that provide evidence of specific sites of plasticity during learning. Fourth, gCAMP imaging
of the granule cell layer will reveal the temporal structure of inputs to the cerebellar microcircuit, and
determine whether those inputs are modified in relation to motor learning. Fifth, a model neural network with
realistic cerebellar architecture will reveal a single set of model parameters that will transform the measured
inputs to the cerebellum in our three movement systems to the measured responses of all neurons in the
cerebellar cortex. Sixth, the model will elucidate how mechanisms of synaptic and cellular plasticity at
different sites in the cerebellar microcircuit work together to cause motor learning.

## Key facts

- **NIH application ID:** 9976609
- **Project number:** 5R01NS112917-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Nicolas Brunel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,275,271
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976609

## Citation

> US National Institutes of Health, RePORTER application 9976609, Canonical computations for motor learning by the cerebellar cortex micro-circuit (5R01NS112917-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9976609. Licensed CC0.

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