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<p>Modulation of dopaminergic signaling has emerged as a central topic in cognitive neuroscience, particularly in relation to neurological and psychiatric disorders. Although numerous computational models have been developed to investigate dopamine-related dynamics, most existing regulation approaches rely on open-loop stimulation strategies or biologically descriptive formulations. Such approaches lack feedback mechanisms and therefore cannot systematically compensate for parameter uncertainties, physiological variability, or external disturbances, which limits their robustness and practical applicability. To address these limitations, this study proposes a robust closed-loop control framework based on super-twisting sliding mode control (ST-SMC) for regulating dopamine release in a microscopic ventral tegmental area (VTA) dopaminergic neuron model. The controller is designed to ensure robustness against significant parameter uncertainties (up to 75%) and external disturbances without requiring explicit system identification. Regulation is implemented at two complementary levels: direct membrane voltage control and firing rate–based control derived from membrane voltage dynamics. The firing rate formulation provides a practically measurable and clinically relevant control objective, particularly when direct membrane voltage regulation is not feasible. Simulation results demonstrate superior performance of the proposed method compared with proportional (P), proportional–integral–derivative (PID), and dual-threshold (DT) controllers. For membrane voltage regulation, the ST-SMC achieves a mean RMSE of 0.31 mV, outperforming P (0.65 mV), DT (17.57 mV), and PID (1.43 mV) controllers. For firing rate control, the proposed approach attains a mean RMSE of 0.67 Hz, compared to 0.94 Hz, 2.06 Hz, and 3.00 Hz for P, DT, and PID, respectively. Although the control energy in the firing rate scenario is slightly higher than that of the P controller, the method provides substantially improved tracking accuracy, reflecting an effective trade-off between precision and stimulation effort. Overall, the proposed ST-SMC framework offers a robust, accurate, and computationally efficient solution for closed-loop regulation of dopaminergic activity.</p>

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Super-twisting sliding mode control of dopamine release in VTA dopaminergic neuron model: a simulation study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Najme Soheilipour,
  • Amir Akhavan,
  • Ehsan Rouhani,
  • Raha Rahimi

摘要

Modulation of dopaminergic signaling has emerged as a central topic in cognitive neuroscience, particularly in relation to neurological and psychiatric disorders. Although numerous computational models have been developed to investigate dopamine-related dynamics, most existing regulation approaches rely on open-loop stimulation strategies or biologically descriptive formulations. Such approaches lack feedback mechanisms and therefore cannot systematically compensate for parameter uncertainties, physiological variability, or external disturbances, which limits their robustness and practical applicability. To address these limitations, this study proposes a robust closed-loop control framework based on super-twisting sliding mode control (ST-SMC) for regulating dopamine release in a microscopic ventral tegmental area (VTA) dopaminergic neuron model. The controller is designed to ensure robustness against significant parameter uncertainties (up to 75%) and external disturbances without requiring explicit system identification. Regulation is implemented at two complementary levels: direct membrane voltage control and firing rate–based control derived from membrane voltage dynamics. The firing rate formulation provides a practically measurable and clinically relevant control objective, particularly when direct membrane voltage regulation is not feasible. Simulation results demonstrate superior performance of the proposed method compared with proportional (P), proportional–integral–derivative (PID), and dual-threshold (DT) controllers. For membrane voltage regulation, the ST-SMC achieves a mean RMSE of 0.31 mV, outperforming P (0.65 mV), DT (17.57 mV), and PID (1.43 mV) controllers. For firing rate control, the proposed approach attains a mean RMSE of 0.67 Hz, compared to 0.94 Hz, 2.06 Hz, and 3.00 Hz for P, DT, and PID, respectively. Although the control energy in the firing rate scenario is slightly higher than that of the P controller, the method provides substantially improved tracking accuracy, reflecting an effective trade-off between precision and stimulation effort. Overall, the proposed ST-SMC framework offers a robust, accurate, and computationally efficient solution for closed-loop regulation of dopaminergic activity.