Embodied Emergence of Temporal Complexity in Finger Tapping: Synaptic and Dopaminergic Control in Cortico-Basal Ganglia Loops
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Finger tapping is a crucial clinical window into the brain’s motor integrity, particularly in Parkinson’s disease (PD). Beyond mere rhythmicity, healthy motor output exhibits long-range correlations (LRCs)—a hallmark of temporal complexity and physiological adaptability. However, how this fractal structure emerges from the interplay between neural circuits and physical body dynamics, and why it collapses under dopamine depletion, remains elusive. Here, we present an embodied computational model integrating the cortico-basal ganglia-thalamocortical (CBGT) loop with a physical finger model under intermittent control. We demonstrate that LRC is not stochastic noise, but emerges from the interaction between physical finger dynamics and intermittent, sensory-driven action selection. Our simulations reveal that while unbiased action selection yields random variability, a sensory-driven striatal bias generates robust LRC, demonstrating that motor complexity can be actively driven by neural “synaptic bias” rather than passive body mechanics. Crucially, simulating PD pathology via dopamine reduction disrupts this bias, leading to a loss of complexity and white-noise transitions that recapitulate a key qualitative feature of the fractal breakdown observed in PD. By bridging neuro-modulation, biomechanical execution, and macroscopic behavior, this work provides a mechanistic framework for embodied neural information processing and offers a potential digital-twin platform for objective biomonitoring in neurodegenerative disorders.
Author summary
Finger tapping may look simple, but the timing of each tap carries rich information about how the brain and body work together. In healthy movement, small variations from one tap to the next are not completely random; instead, they show long-range patterns that are often reduced in Parkinson’s disease. We asked how these patterns arise and why they may disappear when dopamine levels fall. To address this question, we built a computer model that combines neural circuits involved in movement selection with the physical mechanics of the finger. We found that realistic temporal patterns emerged only when sensory information from the moving finger biased the brain’s selection of the next action. When this bias was removed, the tapping rhythm became much more random, even though tapping itself continued. We also found that reducing dopamine progressively disrupted these long-range patterns, reproducing an important qualitative feature of motor impairment in Parkinson’s disease. Our results suggest that healthy movement complexity emerges from continuous interaction between neural decision-making and body mechanics. This framework may eventually help link simple movement measurements to changes in underlying neural function.
