Models that contain the Receptor : D1

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    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 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).
3. Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)
Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter. The reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models. This framework is applicable to modeling ion channels, synaptic release, and all receptors. Please see the references for more details. A simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at and through email:
4. Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.
5. Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015)
In the associated paper (Gurney et al, PLoS Biology, 2015) we presented a computational framework that addresses several issues in cortico-striatal plasticity including spike timing, reward timing, dopamine level, and dopamine receptor type. Thus, we derived a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then showed this model produces the predicted activity changes necessary for learning and extinction in an operant task. Moreover, we showed the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. The model was validated in a wider setting of action selection in basal ganglia, showing how it could account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. The code supplied here allows reproduction of the proposed process of learning in medium spiny neurons, giving the results of Figure 7 of the paper.
6. Dopamine activation of signaling pathways in a medium spiny projection neuron (Oliveira et al. 2012)
Large scale stochastic reaction-diffusion model of signaling pathways in a medium spiny projection neuron dendrite with spines to investigate whether the critical function of anchoring is to locate PKA near the cAMP that activates it or near its targets, such as AMPA receptors located in the post-synaptic density. Simulations, implemented in NeuroRD, show that PKA colocalization with adenylate cyclase, either in the spine head or in the dendrite, leads to greater phosphorylation of DARPP-32 Thr34 and AMPA receptor GluA1 Ser845 than when PKA is anchored away from adenylate cyclase.
7. Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)
We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors
8. 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.
9. Effect of cortical D1 receptor sensitivity on working memory maintenance (Reneaux & Gupta 2018)
Alterations in cortical D1 receptor density and reactivity of dopamine-binding sites, collectively termed as D1 receptor-sensitivity in the present study, have been experimentally shown to affect the working memory maintenance during delay-period. However, computational models addressing the effect of D1 receptor-sensitivity are lacking. A quantitative neural mass model of the prefronto-mesoprefrontal system has been proposed to take into account the effect of variation in cortical D1 receptor-sensitivity on working memory maintenance during delay. The model computes the delay-associated equilibrium states/operational points of the system for different values of D1 receptor-sensitivity through the nullcline and bifurcation analysis. Further, to access the robustness of the working memory maintenance during delay in the presence of alteration in D1 receptor-sensitivity, numerical simulations of the stochastic formulation of the model are performed to obtain the global potential landscape of the dynamics.
10. Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)
The work describes a model of how transient calcium and dopamine inputs might affect phosphorylation of DARPP-32 in the medium spiny neurons in the striatum. The model is relevant for understanding both the "three-factor rule" for synaptic plasticity in corticostriatal synapses, and also for relating reinforcement learning theories to biology.
11. Modeling interactions in Aplysia neuron R15 (Yu et al 2004)
"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades." See paper for more and details.
12. Reproducing infra-slow oscillations with dopaminergic modulation (Kobayashi et al 2017)
" ... In this paper, to reproduce ISO (Infra-Slow Oscillations) in neural networks, we show that dopaminergic modulation of STDP is essential. More specifically, we discovered a close relationship between two dopaminergic effects: modulation of the STDP function and generation of ISO. We therefore, numerically investigated the relationship in detail and proposed a possible mechanism by which ISO is generated."
13. 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.
14. Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)
These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (
15. 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)
16. Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)
To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
17. Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
18. Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.

Re-display model names without descriptions