Mean-field modeling of the basal ganglia-thalamocortical system. I. Firing rates in healthy and parkinsonian states
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Abstract (arXiv)
Parkinsonism leads to various electrophysiological changes in the basal ganglia-thalamocortical system (BGTCS), often including elevated discharge rates of the subthalamic nucleus (STN) and the output nuclei, and reduced activity of the globus pallidus external segment (GPe). These rate changes have been explained qualitatively in terms of the direct/indirect pathway model, involving projections of distinct striatal populations to the output nuclei and GPe. Although these populations partly overlap, evidence suggests dopamine depletion differentially affects cortico-striato-pallidal connection strengths to the two pallidal segments. Dopamine loss may also decrease the striatal signal-to-noise ratio, reducing both corticostriatal coupling and striatal firing thresholds. Here we present a mean-field model of the BGTCS with structure and parameter estimates closely based on physiology and anatomy. Changes in firing rates due to modeled dopamine loss are compared with experiment. Our results suggest that a stronger indirect pathway, possibly combined with a weakened direct pathway, is compatible with empirical evidence. However, altered corticostriatal connection strengths are probably not solely responsible for substantially increased STN activity often found. A lower STN firing threshold, weaker intracortical inhibition, and stronger striato-GPe inhibition help explain the relatively large increase in STN rate. Changes in cortex, GPe, and STN help normalize the cortical rate, also in accord with experiments. The model integrates the basal ganglia into a unified framework along with an existing thalamocortical model that accounts for a wide range of electrophysiological phenomena. A companion paper discusses the dynamics and oscillations of this combined system.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2021A Data-Driven Biophysical Computational Model of Parkinson's Disease based on Marmoset Monkeyscited by 1
- 2025Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Diseasecited by 1
- 2021A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognitioncited by 0
- 2023Learning Control Policies of Hodgkin-Huxley Neuronal Dynamicscited by 0
- 2025Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease26 Apr 2025
- 2023Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics13 Nov 2023
- 2021A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition27 Jul 2021
- 2021A Data-Driven Biophysical Computational Model of Parkinson's Disease based on Marmoset Monkeys27 Jul 2021