Neural trajectories improve motor precision.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Motor Control

Background:

• Classic models assume linear summation of individual neuron activity for movement encoding.
• Recent analyses reveal complex, multi-dimensional neural activity trajectories in motor cortex over time.
• Existing explanations for neural trajectories focus on learning and organization, not performance enhancement.

Purpose of the Study:

• To propose and test a computational model demonstrating how neural trajectories improve motor precision.
• To investigate the role of selective co-activation and timing of neural firing rates in enhancing motor control.
• To link neural trajectory dynamics to improved accuracy in movement execution.

Main Methods:

• Developed a computational model inspired by experimental center-out reaching tasks.
• Created physiologically realistic models for neural encoding of movement.
• Utilized a recurrent neural network to simulate downstream neural processing (e.g., spinal cord, motor units).

Main Results:

• Movements were more accurate when incorporating time-varying neural information (phase/amplitude) compared to instantaneous velocity-only models.
• The model demonstrated that precise motor control emerges from the spatiotemporal recruitment of neural populations.
• Distinct neural trajectories, formed by selective neural population activity, were shown to enhance motor precision.

Conclusions:

• Precise motor control is achieved through the dynamic, spatiotemporal recruitment of neural populations generating distinct neural trajectories.
• Findings challenge traditional linear summation models of neural encoding for movement.
• Results offer insights into brain network communication for movement planning and execution, and inspire brain-computer interface improvements.