Embodied Emergence of Temporal Complexity in Finger Tapping: Synaptic and Dopaminergic Control in Cortico-Basal Ganglia Loops
- 1Kyoto University
Abstract
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 neuromodulation, 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.
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BibTeX
@misc{tigrini2026embodied,
title = {{Embodied Emergence of Temporal Complexity in Finger Tapping: Synaptic and Dopaminergic Control in Cortico-Basal Ganglia Loops}},
author = {A. Tigrini and H. Shimokado and K. Matsui and A. Mengarelli and R. Mobarak and M. Scattolini and F. Verdini and L. Burattini and T. Nomura},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.07.749738},
url = {https://www.biorxiv.org/content/10.64898/2026.09.07.749738v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Embodied Emergence of Temporal Complexity in Finger Tapping: Synaptic and Dopaminergic Control in Cortico-Basal Ganglia Loops
AU - A. Tigrini
AU - H. Shimokado
AU - K. Matsui
AU - A. Mengarelli
AU - R. Mobarak
AU - M. Scattolini
AU - F. Verdini
AU - L. Burattini
AU - T. Nomura
PY - 2026
DA - 2026/09/07
PB - bioRxiv
DO - 10.64898/2026.09.07.749738
UR - https://www.biorxiv.org/content/10.64898/2026.09.07.749738v1
AB - 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 neuromodulation, 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.
ER -