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Related Experiment Video

Updated: Mar 22, 2026

Measuring and Manipulating Functionally Specific Neural Pathways in the Human Motor System with Transcranial Magnetic Stimulation

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Robust neuronal dynamics in premotor cortex during motor planning.

Nuo Li¹, Kayvon Daie¹, Karel Svoboda¹

¹Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA.

|April 14, 2016

Summary

Neural representations bridge events, but positive feedback can cause instability. Mouse premotor cortex neural activity robustly restores movement plans after unilateral silencing, highlighting interhemispheric compensation.

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Area of Science:

Neuroscience
Systems Neuroscience
Motor Control

Background:

Neural activity sustains representations bridging past and future events over seconds.
Network models with positive feedback can exhibit slow dynamics but are sensitive to perturbations.

Purpose of the Study:

To investigate the robustness of persistent neural representations in the mouse premotor cortex during motor planning.
To understand how neural networks recover from perturbations affecting preparatory activity.

Main Methods:

Electrophysiology and optogenetic perturbations were used in the mouse premotor cortex.
Unilateral and bilateral silencing of the premotor cortex was performed.
Corpus callosum bisection was utilized to assess interhemispheric communication.

Main Results:

Preparatory neural activity driving specific movements was rapidly and selectively restored after unilateral silencing.
Selectivity of preparatory activity was not recovered following bilateral silencing.
Premotor cortex hemispheres demonstrated independent maintenance of preparatory activity.

Conclusions:

The mouse premotor cortex exhibits remarkable robustness in neural representations during motor planning, with interhemispheric compensation playing a key role.
Redundancy across selectively coupled modules in the premotor cortex contributes to robust neural control.
Network models incorporating these principles can explain the observed robustness of neural dynamics.