Inferring when to move.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Motor Control

Background:

• Human movement involves discrete actions at regular intervals, yet models of motor timing are limited.
• Understanding how the brain determines action intervals is crucial for explaining motor control.

Purpose of the Study:

• To theoretically analyze the problem of motor timing and action interval determination.
• To propose a computational model based on internal clocks for timing discrete movements.
• To investigate the model's relevance to neurological disorders, specifically Parkinson's disease.

Main Methods:

• Developed a theoretical framework for motor timing based on generative world models with internal clocks of varying speeds.
• Modeled the alignment of alternating movements with external auditory stimuli.
• Simulated the effects of disrupting specific parameters related to prior inferences in the brain's timing mechanism.

Main Results:

• The model explains how discrete actions can emerge from continuous processes by inferring the best-fitting internal clock.
• Simulations successfully reproduced core behavioral and electrophysiological features of Parkinson's disease, including amplitude decrement, festination, and breakdown of repetitive movements.
• Disrupting parameters controlling prior inferences mimicked parkinsonian symptoms.

Conclusions:

• The proposed internal clock model offers a mechanistic interpretation of motor timing.
• The framework provides insights into how disruptions in neural timing mechanisms contribute to Parkinson's disease.
• The model may inform the development of therapeutics targeting motor deficits in Parkinson's disease.