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Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model
Ludovica Bachschmid-Romano1, Nicholas G Hatsopoulos2,3, Nicolas Brunel1,4,5,6
1Department of Neurobiology, Duke University, Durham, United States.
Elife
|May 11, 2023
Summary
This study models the primary motor cortex to understand how neural networks control movement. It reveals that external inputs shape preparation, while recurrent connections drive movement execution for effective motor control.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Motor Control
Background:
- The primary motor cortex (M1) coordinates movement preparation and execution.
- Understanding the network mechanisms, particularly external and recurrent connectivity, is crucial for deciphering motor control.
- The roles of these connections in neural computations remain open questions.
Purpose of the Study:
- To develop a recurrent neural network (RNN) model that replicates primary motor cortex activity during a delayed-reach task.
- To investigate how synaptic connectivity structures and external inputs contribute to observed neural dynamics.
- To explain the orthogonality of preparatory and movement-related subspaces.
Main Methods:
- Developed a recurrent neural network model.
- Used neuronal activity data from a macaque monkey performing an instructed delayed-reach task.
- Constrained model parameters (synaptic connectivity, external input) using recorded data.
- Explored different combinations of feedforward and recurrent connections.
Main Results:
- The RNN model successfully recapitulated the temporal evolution of neural activity and dynamic patterns of neural-movement direction covariation.
- The model identified a specific connectivity solution requiring minimal external input.
- This solution showed external inputs shaping preparatory activity and strong, direction-specific recurrent connectivity driving movement execution.
- Demonstrated how changing single-neuron tuning properties over time explain subspace orthogonality.
Conclusions:
- Recurrent connectivity and external inputs play distinct, critical roles in motor preparation and execution.
- The interplay between external inputs during preparation and recurrent connections during execution is key to motor control.
- Dynamic changes in neural tuning properties are essential for generating orthogonal representations in motor cortex subspaces.
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