# A new theory of population coding in the cerebellum

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2020 · $1,248,553

## Abstract

A theory of population coding in the cerebellum
In order to move accurately, the brain relies on internal models that predict the sensory consequences of motor
commands. Evidence for this idea comes from human behavioral experiments [1-7] and animal lesion studies [8-
11], suggesting that the critical structure for forming internal models is the cerebellum. However, in the cerebellum
it is often difficult to relate spiking activity of individual Purkinje cells (P-cells) with behavior: while for some tasks
like smooth pursuit eye movements the activity of P-cells is a simple function of eye velocity [12], for most other
movements such as saccades [13,14], wrist movements [15], or arm movements [16-19], it is difficult to associate
activity of individual P-cells to behavior. Anatomy of the cerebellum suggests that P-cells organize in small groups,
together projecting onto a single output nucleus neuron [20]. This anatomy implies that the fundamental
computational unit of the cerebellum is not a single P-cell, but a population of P-cells that together converges onto
a single output neuron. Thus, population coding in the cerebellum has a specific anatomical meaning: P-cells that
converge onto a single output neuron together encode an aspect of behavior [21]. The critical problem is to
identify the membership of each population in the living brain. Recently, we demonstrated a way to approach this
problem [22]: P-cells that share the same complex spike tuning likely belong to the same population. However,
identification of complex spike tuning is exceptionally difficult: complex spikes are rare events that have variable
waveform durations. Indeed, the current approach relies on manual labeling of complex spikes, something that
cannot be scaled to multi-contact probes. Here, three labs with expertise in marmosets, mice, and macaques have
come together to develop algorithms that automate detection and attribution of complex spikes. These algorithms
focus on the frequency-domain classification of spikes, and will be tested on high density multi-contact probes.
Together, the algorithms and experimental procedures should significantly improve the ability of neuroscientists to
tackle the question of population coding in the cerebellum, ultimately resulting in better understanding of how the
cerebellum learns to precisely control movements of our body.

## Key facts

- **NIH application ID:** 10005617
- **Project number:** 1R01EB028156-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** REZA SHADMEHR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,248,553
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005617

## Citation

> US National Institutes of Health, RePORTER application 10005617, A new theory of population coding in the cerebellum (1R01EB028156-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10005617. Licensed CC0.

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