| | Models | Description |
| 1. |
A basal ganglia model of aberrant learning (Ursino et al. 2018)
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A comprehensive, biologically inspired neurocomputational model of action selection in the Basal Ganglia allows simulation of dopamine induced aberrant learning in Parkinsonian subjects. In particular, the model simulates the Alternate Finger Tapping motor task as an indicator of bradykinesia. |
| 2. |
A contracting model of the basal ganglia (Girard et al. 2008)
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Basal ganglia model : selection processes between channels, dynamics controlled by contraction analysis, rate-coding model of neurons based on locally projected dynamical systems (lPDS). |
| 3. |
Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
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We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: • The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. • STN and MSN target more the GPe than the GPi/SNr. • Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern. |
| 4. |
Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
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See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions. |
| 5. |
Levodopa-Induced Toxicity in Parkinson's Disease (Muddapu et al, 2022)
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"... We present a systems-level computational model of SNc-striatum, which will help us understand the mechanism behind neurodegeneration postulated above and provide insights into developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During L-DOPA therapy, it was observed that higher L-DOPA dosage results in increased loss of terminals in SNc. It was also observed that co-administration of L-DOPA and glutathione (antioxidant) evades L-DOPA-induced toxicity in SNc neurons. Our proposed model of the SNc-striatum system is the first of its kind, where SNc neurons were modeled at a biophysical level, and striatal neurons were modeled at a spiking level. We show that our proposed model was able to capture L-DOPA-induced toxicity in SNc, caused by energy deficiency." |
| 6. |
Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
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Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007). |
| 7. |
Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
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A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models. |
| 8. |
Spiking neuron model of the basal ganglia (Humphries et al 2006)
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A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations. |
| 9. |
Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
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This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity. |
| 10. |
Synchronicity of fast-spiking interneurons balances medium-spiny neurons (Damodaran et al. 2014)
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This study investigates the role of feedforward and feedback inhibition in maintaining the balance between D1 and D2 MSNs of the striatum. The synchronized firing of FSIs are found to be critical in this mechanism and specifically the gap junction connections between FSIs. |
| 11. |
The microcircuits of striatum in silico (Hjorth et al 2020)
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"Our aim is to reconstruct a full-scale mouse striatal cellular level model to provide a framework to integrate and interpret striatal data. We represent the main striatal neuronal subtypes, the two types of projection neurons (dSPNs and iSPNs) giving rise to the direct and indirect pathways, the fast-spiking interneurons, the low threshold spiking interneurons, and the cholinergic interneurons as detailed compartmental models, with properties close to their biological counterparts. Both intrastriatal and afferent synaptic inputs (cortex, thalamus, dopamine system) are optimized against existing data, including short-term plasticity. This model platform will be used to generate new hypotheses on striatal function or network dynamic phenomena." |
