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| Models | Description |
| 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). | |
| 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 The classical model of the BG that allowed for the discovery of new treatments for PD seems today outdated in several respects.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. | |
| 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." | |
| 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." | |
| 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. | |
| 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. | |
| 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. ..." | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. ..." | |
| 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." | |
| 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”. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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." | |
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