Models that contain the Region : Basal ganglia

Re-display model names without descriptions

	Models	Description
1.	A basal ganglia model of aberrant learning (Ursino et al. 2018)
		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 computational model of action selection in the basal ganglia (Suryanarayana et al 2019)
		" ... Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or ‘arkypallidal’ neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity — revealing a possible link with symptoms of Parkinson’s disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action ‘channel’ was, under some circumstances, reversed. ..."
3.	A contracting model of the basal ganglia (Girard et al. 2008)
		Basal ganglia model : selection processes between channels, dynamics controlled by contraction analysis, rate-coding model of neurons based on locally projected dynamical systems (lPDS).
4.	A dynamical model of the basal ganglia (Leblois et al 2006)
		We propose a new model for the function and dysfunction of the basal ganglia (BG). The basal ganglia are a set of cerebral structures involved in motor control which dysfunction causes high-incidence pathologies such as Parkinson's disease (PD). Their precise motor functions remain unknown. The classical model of the BG that allowed for the discovery of new treatments for PD seems today outdated in several respects. Based on experimental observations, our model proposes a simple dynamical framework for the understanding of how BG may select motor programs to be executed. Moreover, we explain how this ability is lost and how tremor-related oscillations in neuronal activity may emerge in PD.
5.	A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity (Nakano et al. 2010)
		A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction."
6.	A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)
		" ... In this work, we present a 2.5-million-neuron model of the brain (called “Spaun”) that bridges this gap (between neural activity and biological function) by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks."
7.	Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)
		"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.
8.	Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)
		Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.
9.	Basal ganglia motor function and the inverse kinematics calculation (Salimi-Badr et al 2017)
		The computational model to study the possible correlation between Basal Ganglia (BG) function and solving the Inverse Kinematics (IK).
10.	Basal Ganglia motor-circuit for kinematic planning of arm movements (Salimi-Badr et al 2017)
		A mathematical model of BG for kinematic planning.
11.	Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
		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.
12.	Basal ganglia-corticothalamic (BGCT) network (Chen et al., 2014)
		We developed a biophysical model of the basal ganglia-corticothalamic network in this work. "... We demonstrate that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. ... results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder."
13.	Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
		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.
14.	Biophysical basis of Subthalamic LFPs Recorded from DBS electrodes (Maling et al 2018)
		"Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient’s unique brain anatomy and electrode placement impact the recordings. Therefore, we developed a patient-specific computational framework to analyze LFP recordings within a clinical DBS context. We selected a subject with Parkinson’s disease implanted with a Medtronic Activa PC+S DBS system and reconstructed their subthalamic nucleus (STN) and DBS electrode location using medical imaging data. The patient-specific STN volume was populated with 235,280 multicompartment STN neuron models, providing a neuron density consistent with histological measurements. Each neuron received time-varying synaptic inputs and generated transmembrane currents that gave rise to the LFP signal recorded at DBS electrode contacts residing in a finite element volume conductor model. We then used the model to study the role of synchronous beta-band inputs to the STN neurons on the recorded power spectrum. ..."
15.	Cognitive and motor cortico-basal ganglia interactions during decision making (Guthrie et al 2013)
		This is a re-implementation of Guthrie et al 2013 by Topalidou and Rougier 2015. The original study investigated how multiple level action selection could be performed by the basal ganglia.
16.	Computational endophenotypes in addiction (Fiore et al 2018)
		"... here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. ..."
17.	Computational modeling of ultrasonic Subthalamic Nucleus stimulation (Tarnaud et al 2019)
		"Objective: To explore the potential of ultrasonic modulation of plateau-potential generating subthalamic nucleus neurons (STN), by modeling their interaction with continuous and pulsed ultrasonic waves. Methods: A computational model for ultrasonic stimulation of the STN is created by combining the Otsuka-model with the bilayer sonophore model. The neuronal response to continuous and pulsed ultrasonic waves is computed in parallel for a range of frequencies, duty cycles, pulse repetition frequencies, and intensities. ..."
18.	Control of oscillations and spontaneous firing in dopamine neurons (Rumbell & Kozloski 2019)
		Model of Substantia Nigra pars Compacta Dopamine Neuron. 'Toy' morphology with 4 dendrites, one of which is the axon-bearing dendrite, with an axon branching from it. The axon is a short 'axon initial segment' compartment, followed by a longer 'axon'. 727 parameter sets for ion channel conductance and kinetic parameters were found using evolutionary optimization, all of which are viable candidates representing a plausible model of a SNc DA.
19.	Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
		" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ..."
20.	Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
		We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity.
21.	Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
		"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. ..."
22.	Cortico - Basal Ganglia Loop (Mulcahy et al 2020)
		The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions
23.	Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)
		To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
24.	Dopaminergic subtantia nigra neuron (Moubarak et al 2019)
		Axon initial segment (AIS) geometry critically influences neuronal excitability. Interestingly, the axon of substantia nigra pars compacta (SNc) dopaminergic (DA) neurons displays a highly variable location and most often arises from an axon-bearing dendrite (ABD). We combined current-clamp somatic and dendritic recordings, outside-out recordings of dendritic sodium and potassium currents, morphological reconstructions and multi-compartment modelling to determine cell-to-cell variations in AIS and ABD geometry and their influence on neuronal output (spontaneous pacemaking frequency, AP shape). Both AIS and ABD geometries are highly variable between SNc DA neurons. Surprisingly, we found that AP shape and pacemaking frequency were independent of AIS geometry. Modelling realistic morphological and biophysical variations clarify this result: in SNc DA neurons, the complexity of the ABD combined with its excitability predominantly define pacemaking frequency and AP shape, such that large variations in AIS geometry negligibly affect neuronal output, and are tolerated.
25.	Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
		See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.
26.	Effects of Dopamine Modulation and KIR Inactivation in NAc Medium Spiny Neurons (Steephen 2011)
		Due to the involvement of nucleus accumbens (NAc) medium spiny neurons (MSNs) in diverse behaviors, their excitability changes can have broad functional significance. Dopamine modulates the biophysical behavior of MSNs. In ~40% of MSNs, inward rectifying potassium (KIR) currents inactivate significantly, imparting greater excitability. Employing a 189-compartment computational model of the MSN and using spatiotemporally distributed synaptic inputs, the regulation of excitability by KIR inactivation and dopaminergic modulation was investigated and quantitatively characterized. It was shown that by forming different combinations, these regulating agents could fine tune MSN excitability across a wide range. With existing evidence indicating MSNs with and without KIR inactivation to be the likely targets for D2- and D1-receptor mediated modulations, respectively, the present findings suggest that dopaminergic channel modulation may intensify the existing excitability difference between them by suppressing the excitability of MSNs without KIR inactivation while further enhancing the excitability of the more excitable MSNs with KIR inactivation. On the other hand, the combined modulation of channels and synapses by dopamine may reverse the relative excitability of one cell type with respect to the other. This model contains a complete biophysical model of MSN cell. The application allows the user to vary the cell properties by choosing the type of KIR channels included (inKIR or non-inKIR), the type of Dopamine receptors (D1R or D2R) and the modulation mechanism (Intrinsic modulation , Intrinsic-synaptic modulation, or No modulation). The user can also choose between the single pulse current clamp stimulation or a physiologically realistic synaptic stimulation scheme. More details are available in the Help provided with the application.
27.	Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
		"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..."
28.	Excessive beta oscillations in Parkinson's disease (Pavlides et al. 2015)
		" ... Understanding the generation of beta oscillations is important to improve treatments for Parkinson’s disease. Competing theories exist for how these oscillations are generated in the affected brain circuits, which include the motor cortex and a set of subcortical nuclei called the basal ganglia. This paper suggests two hypotheses for the generation of beta oscillations. The first hypothesis is that beta oscillations are generated in the motor cortex, and the basal ganglia resonate to the cortical input. The second hypothesis additionally proposes that feedback from the basal ganglia to cortex is critically important for the presence of the oscillations. We show that the models can successfully account for a wide range of experimental data concerning the presence of beta oscillations in Parkinson’s disease."
29.	Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)
		"... A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD (Parkinson's disease) therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model."
30.	Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
		"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... 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.
31.	Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)
		Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
32.	Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)
		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.
33.	GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)
		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.
34.	High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
		" ... 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. ..."
35.	Information trans. through Entopeduncular nucleus modified by synaptic plasticity (Gorodetsky et al)
		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.
36.	Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
		"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. ... 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."
37.	Levodopa-Induced Toxicity in Parkinson's Disease (Muddapu et al, 2022)
		"... 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."
38.	LFP in striatum (Tanaka & Nakamura 2019)
		The numerical simulations of LFP generation by cortical pyramidal neuron and medium-sized spiny neurons.
39.	Library of biophysically detailed striatal projection neurons (Lindroos and Hellgren Kotaleski 2020)
		Library of compartmentalized models used to investigate dendritic integration in striatal projection neurons under neuromodulation.
40.	Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)
		"In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability."
41.	Mechanisms for pattern specificity of DBS in Parkinson's disease (Velarde et al 2017)
42.	Model for K-ATP mediated bursting in mSNc DA neurons (Knowlton et al 2018)
		"Burst firing in medial substantia nigra dopamine (mSN DA) neurons has been selectively linked to novelty-induced exploration behavior in mice. Burst firing in mSN DA neurons, in contrast to lateral SN DA neurons, requires functional ATP-sensitive potassium channels (K-ATP) both in vitro and in vivo. However, the precise role of K-ATP channels in promoting burst firing is un-known. We show experimentally that L-type calcium channel activity in mSN DA neurons en-hances open probability of K-ATP channels. We then generated a mathematical model to study the role of Ca2+ dynamics driving K-ATP channel function in mSN DA neurons during bursting. ..."
43.	Morphological determinants of action potential dynamics in substantia nigra (Moubarak et al 2022)
		This model allows to simulate pacemaking activity in 37 fully reconstructed neurons. Calcium and sodium conductances vary by 11 increments in the Axon bearing dendrite part to simulate a 11*11*37 models. For each model Action potential (AP) properties are measured : frequency, amplitude, Threshold, Half duration, max first and second derivative. AP and conductances traces are then saved in a csv file.
44.	Multiscale model of excitotoxicity in PD (Muddapu and Chakravarthy 2020)
		Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The proposed model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss.
45.	NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)
		Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
46.	Network model of movement disorders (Yousif et al 2020)
		This is a Wilson-Cowan model of the basal ganglia thalamocortical cerebellar network that demonstrates healthy gamma band oscillations, Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arising from the dynamics of the network.
47.	Nicotinic control of dopamine release in nucleus accumbens (Maex et al. 2014)
		Minimal model of the VTA (ventral segmental area) representing two (GABA versus dopamine) neuron populations and two subtypes of nicotinic receptors (alpha4beta2 versus alpha7). The model is used to tell apart circuit from receptor mechanisms in the nicotinic control of dopamine release and its pharmacological manipulation.
48.	Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
		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.
49.	Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
		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.
50.	Phase response curve of a globus pallidal neuron (Fujita et al. 2011)
		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.
51.	Phasic dopamine changes, Hebbian mechs during reversal learning in striatum (Schirru et al in press)
		A model simulating probabilistic action selection in the basal ganglia and reversal learning, with the possibility to use different versions of the Hebb rule and a flexible behavior for dopamine phasic changes
52.	Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004)
		We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation. In the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models.
53.	Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)
		A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.
54.	Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)
		Computational modeling showed that the size of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators.
55.	Signaling pathways In D1R containing striatal spiny projection neurons (Blackwell et al 2018)
		We implemented a mechanistic model of signaling pathways activated by dopamine D1 receptors, acetylcholine receptors, and glutamate. We use our novel, computationally efficient simulator, NeuroRD, to simulate stochastic interactions both within and between dendritic spines. Results show that the combined activity of several key plasticity molecules correctly predicts the occurrence of either LTP, LTD or no plasticity for numerous experimental protocols.
56.	Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
		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.
57.	Single-cell comprehensive biophysical model of SN pars compacta (Muddapu & Chakravarthy 2021)
		Parkinson’s disease (PD) is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that “Energy deficiency at a sub-cellular/cellular/systems-level can be a common underlying cause for SNc cell loss in PD.” Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular-level in PD. We were able to show see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model also suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD.
58.	Speed/accuracy trade-off between the habitual and the goal-directed processes (Kermati et al. 2011)
		"This study is a reference implementation of Keramati, Dezfouli, and Piray 2011 that proposed an arbitration mechanism between a goal-directed strategy and a habitual strategy, used to model the behavior of rats in instrumental conditionning tasks. The habitual strategy is the Kalman Q-Learning from Geist, Pietquin, and Fricout 2009. We replicate the results of the first task, i.e. the devaluation experiment with two states and two actions. ..."
59.	Spiking neuron model of the basal ganglia (Humphries et al 2006)
		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.
60.	Striatal D1R medium spiny neuron, including a subcellular DA cascade (Lindroos et al 2018)
		We are investigating how dopaminergic modulation of single channels can be combined to make the D1R possitive MSN more excitable. We also connect multiple channels to substrates of a dopamine induced subcellular cascade to highlight that the classical pathway is too slow to explain DA induced kinetics in the subsecond range (Howe and Dombeck, 2016. doi: 10.1038/nature18942)
61.	Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018)
		We use a computational model of a striatal spiny projection neuron to investigate dendritic spine calcium dynamics in response to spatiotemporal patterns of synaptic inputs. We show that spine calcium elevation is stimulus-specific, with supralinear calcium elevation in cooperatively stimulated spines. Intermediate calcium elevation occurs in neighboring non-stimulated dendritic spines, predicting heterosynaptic effects. Inhibitory synaptic inputs enhance the difference between peak calcium in stimulated spines, and peak calcium in non-stimulated spines, thereby enhancing stimulus specificity.
62.	Striatum D1 Striosome and Matrix Upstates (Prager et al., 2020)
		"...We show that dopamine oppositely shapes responses to convergent excitatory inputs in mouse striosome and matrix striatal spiny projection neurons (SPNs). Activation of postsynaptic D1 dopamine receptors promoted the generation of long-lasting synaptically evoked 'up-states' in matrix SPNs but opposed it in striosomes, which were more excitable under basal conditions. Differences in dopaminergic modulation were mediated, in part, by dendritic voltage-gated calcium channels (VGCCs): pharmacological manipulation of L-type VGCCs reversed compartment-specific responses to D1 receptor activation..."
63.	Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)
		" ... 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."
64.	The microcircuits of striatum in silico (Hjorth et al 2020)
		"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."
65.	The STN-GPe network; subthalamic nucleus, prototypic GPe, and arkypallidal GPe neurons (Kitano 2023)
66.	VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
		In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials.

Re-display model names without descriptions