ArticleJournal of computational neuroscience2013
A fast model of voltage-dependent NMDA receptors.
Article in Journal of computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- KChIP4a is a Biophysical Amplifier of Inhibition in Atypical Dopamine Neurons and Controls Learning from Negative Prediction Errors.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Modeling cortical synaptic effects of anesthesia and their cholinergic reversal.PLoS computational biology · 2022Article
- Common synaptic phenotypes arising from diverse mutations in the human NMDA receptor subunit GluN2A.Communications biology · 2022Article
- Coincident glutamatergic depolarizations enhance GABAA receptor-dependent Cl- influx in mature and suppress Cl- efflux in immature neurons.PLoS computational biology · 2021Article
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- Assessing the Effects of Opioids on Pathological Memory by a Computational Model.Basic and clinical neuroscienceArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
NMDA receptors are among the crucial elements of central nervous system models. Recent studies show that both conductance and kinetics of these receptors are changing voltage-dependently in some parts of the brain. Therefore, several models have been introduced to simulate their current. However, on the one hand, kinetic models-which are able to simulate these voltage-dependent phenomena-are computationally expensive for modeling of large neural networks. On the other hand, classic exponential models, which are computationally less expensive, are not able to simulate the voltage-dependency of these receptors, accurately. In this study, we have modified these classic models to endow them with the voltage-dependent conductance and time constants. Temperature sensitivity and desensitization of these receptors are also taken into account. We show that, it is possible to simulate the most important physiological aspects of NMDA receptor's behavior using only three to four differential equations, which is significantly smaller than the previous kinetic models. Consequently, it seems that our model is both fast and physiologically plausible and therefore is a suitable candidate for the modeling of large neural networks.
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Registered trials
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