# Large-scale recording of spike train ensembles from muscle fibers during skilled behavior in mice and songbirds

> **NIH NIH R01** · EMORY UNIVERSITY · 2021 · $30,962

## Abstract

A crucial problem in neuroscience is to understand how the brain coordinates muscle activity to produce
behavior. For decades, researchers have approached this problem by recording spiking activity in motor areas
and attempting to understand how cortical spike trains control motor output, which is characterized either by
the behavior itself (e.g. hand trajectories) or by standard EMG methods (which provide a bulk, multiunit
measure of muscle activity levels). Recent advances in both neural recording hardware now allow spike trains
from a large number of neurons to be recorded simultaneously. Furthermore, novel computational methods for
analyzing large datasets of concurrently-recorded neural activity have led to dramatic new insights into how
cortical networks encode behavior. However, there are currently no methods for recording large spiking
ensembles in muscle cells during behavior, hindering our understanding of how the nervous system ultimately
coordinates movement. This unmet need hinders our understanding of brain function, both from the
perspective of comprehending the normal function of the nervous system and in designing clinical applications
that might use EMG activity to enable patients to control prostheses. This proposal builds on the recent
invention (by PI Sober's group) of a novel class of flexible electrode array that sits on the muscle surface and
provides single-unit recordings of individual motor units, the collection of muscle fibers innervated by a single
motor neuron. We will build on these preliminary results to develop and validate technologies for recording the
entire ensemble of muscles in both the songbird vocal organ and the mouse forelimb. To do so we will
advance the current design by scaling it up the channel count by orders of magnitude, developing species-
specific array morphologies that can record units from all relevant muscles simultaneously, embedding active
circuitry within the flexible array to allow data multiplexing, and creating and distributing software tools for
processing and analyzing the resulting datasets.

## Key facts

- **NIH application ID:** 10303478
- **Project number:** 3R01NS109237-02S2
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Muhannad  Bakir
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $30,962
- **Award type:** 3
- **Project period:** 2021-01-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10303478

## Citation

> US National Institutes of Health, RePORTER application 10303478, Large-scale recording of spike train ensembles from muscle fibers during skilled behavior in mice and songbirds (3R01NS109237-02S2). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10303478. Licensed CC0.

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