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Updated: May 10, 2026
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In Vivo Wireless Optogenetic Control of Skilled Motor Behavior
Published on: November 22, 2021
Heterogeneous attractor cell assemblies for motor planning in premotor cortex.
Maurizio Mattia1, Pierpaolo Pani, Giovanni Mirabella
1Department of Technologies and Health, Istituto Superiore di Sanità, 00161 Rome, Italy. maurizio.mattia@iss.it
Summary
Sudden transitions in monkey dorsal premotor cortex (PMd) activity predict actions, even when movements are delayed or canceled. This suggests motor plans arise from interconnected neuronal modules, not just execution.
Area of Science:
- Neuroscience
- Cognitive Science
- Computational Neuroscience
Background:
- Motor planning involves complex neuronal activity.
- The dorsal premotor cortex (PMd) plays a crucial role in action preparation.
- Computational strategies underlying motor planning remain largely unknown.
Purpose of the Study:
- To investigate the neuronal basis of motor planning in the dorsal premotor cortex (PMd).
- To identify predictive signals for forthcoming actions on a single-trial basis.
- To test an attractor model for explaining observed neuronal dynamics.
Main Methods:
- Recorded multiunit activity and field potentials in the dorsal premotor cortex (PMd) of monkeys during reaching tasks.
- Utilized behavioral paradigms involving delayed and canceled movements.
- Developed and analyzed an attractor model to simulate neuronal dynamics.
Main Results:
- Observed stereotyped sudden transitions (STs) between low and high multiunit activity in PMd, predicting actions.
- STs occurred irrespective of movement execution, delay, or cancellation.
- An attractor model successfully explained STs and high-frequency modulations, suggesting synaptic reverberation.
- Motor plans emerge from coactivation of heterogeneous attractor modules with varying self-excitation strengths.
Conclusions:
- Motor plans in PMd are formed by the coordinated activity of interconnected attractor modules.
- These modules exhibit a "flip-flop" dynamic, rapidly converging to a distributed representation of the motor program.
- Findings support the theory of associative networks in motor control.
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