| | Models | Description |
| 1. |
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). |
| 2. |
Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)
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"Based on recent experimental data, we have developed a
conductance-based computational network model of the subthalamic
nucleus and the external segment of the globus pallidus
in the indirect pathway of the basal ganglia. Computer
simulations and analysis of this model illuminate the roles of the
coupling architecture of the network, and associated synaptic
conductances, in modulating the activity patterns displayed by
this network. Depending on the relationships of these coupling
parameters, the network can support three general classes of
sustained firing patterns: clustering, propagating waves, and
repetitive spiking that may show little regularity or correlation. ...". Terman's XPP code and a partial implementation by Taylor Malone in NEURON and python are included. |
| 3. |
Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
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Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article
Instructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading. |
| 4. |
Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)
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This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency. |
| 5. |
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. |
| 6. |
Comparison of full and reduced globus pallidus models (Hendrickson 2010)
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In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models. |
| 7. |
Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
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"Although brain oscillations involving the basal ganglia (BG) have been
the target of extensive research, the main focus lies
disproportionally on oscillations generated within the BG circuit
rather than other sources, such as cortical areas. We remedy this here
by investigating the influence of various cortical frequency bands on
the intrinsic effective connectivity of the BG, as well as the role of
the latter in regulating cortical behaviour. To do this, we construct
a detailed neural model of the complete BG circuit based on fine-tuned
spiking neurons, with both electrical and chemical synapses as well as
short-term plasticity between structures. As a measure of effective
connectivity, we estimate information transfer between nuclei by means
of transfer entropy. Our model successfully reproduces firing and
oscillatory behaviour found in both the healthy and Parkinsonian
BG. We found that, indeed, effective connectivity changes dramatically
for different cortical frequency bands and phase offsets, which are
able to modulate (or even block) information flow in the three major
BG pathways. ..." |
| 8. |
Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
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"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS).
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This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients.
…"
Implemented by Andrey Dovzhenok, to whom questions should be addressed. |
| 9. |
Globus pallidus multi-compartmental model neuron with realistic morphology (Gunay et al. 2008)
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"Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties.
To investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations.
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Our results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments.
Further analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present.
In some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures.
..." |
| 10. |
Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)
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A set of 9 multi-compartmental rat GP neuron models (585 compartments) differing only in their expression of dendritic fast sodium channels were compared in their synaptic integration properties. Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation. |
| 11. |
GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)
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Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs. |
| 12. |
GPi/GPe neuron models (Johnson and McIntyre 2008)
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Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN. |
| 13. |
High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
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" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic
basal ganglia output with tonic, high frequency firing.
In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals.
High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result.
We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in
normal, parkinsonian, and DBS states in a reduced model.
This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ..."
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| 14. |
Information trans. through Entopeduncular nucleus modified by synaptic plasticity (Gorodetsky et al)
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Multicompartmental model of EP neuron was created using automatic parameter optimization. We included both short term plasticity and long term plasticity. We simulated the response to inputs from globus pallidus, striatum and subthalamic nucleus. We show that dopamine long term plasticity enhances information transmission from striatum and reduces GPe and STN information transmission. |
| 15. |
Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
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"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe).
A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed.
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Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively.
Our results are consistent with the experimental and the clinical evidences on the network effects of DBS."
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| 16. |
Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)
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" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold.
This change made the neuron more sensitive to intrinsically generated noise.
In vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs."
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| 17. |
Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007)
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Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66. |
| 18. |
Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
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Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms. |
| 19. |
Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
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This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored. |
| 20. |
Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)
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"Computational models in neuroscience can be used to predict causal
relationships between biological mechanisms in neurons and networks,
such as the effect of blocking an ion channel or synaptic connection
on neuron activity. Since developing a biophysically realistic, single
neuron model is exceedingly difficult, software has been developed for
automatically adjusting parameters of computational neuronal
models. The ideal optimization software should work with commonly used
neural simulation software; thus, we present software which works with
models specified in declarative format for the MOOSE
simulator. Experimental data can be specified using one of two
different file formats. The fitness function is customizable as a
weighted combination of feature differences. The optimization itself
uses the covariance matrix adaptation-evolutionary strategy, because
it is robust in the face of local fluctuations of the fitness
function, and deals well with a high-dimensional and discontinuous
fitness landscape. We demonstrate the versatility of the software by
creating several model examples of each of four types of neurons (two
subtypes of spiny projection neurons and two subtypes of globus
pallidus neurons) by tuning to current clamp data.
..." |
| 21. |
Phase response curve of a globus pallidal neuron (Fujita et al. 2011)
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We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current. |
| 22. |
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). |
| 23. |
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. |
| 24. |
Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)
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" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD." |
| 25. |
The STN-GPe network; subthalamic nucleus, prototypic GPe, and arkypallidal GPe neurons (Kitano 2023)
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