期刊
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷 16, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2022.806086
关键词
decay time constant; double exponential fitting; NMDA receptors; AMPA receptors; postsynaptic current; conductance-based models
资金
- Swedish National Infrastructure for Computing (SNIC) at PDC KTH - Swedish Research Council [VR-M-2017-02806]
- European Union [VR-M-2020-01652]
- Swedish Research Council [VR-M-2017-02806, VR-M-2020-01652]
- Swedish e-Science Research Centre (SeRC) [945539]
- KTH Digital Futures to JK, Wallenberg Academy Fellow prolongation [KAW 2017.0273]
- Hjaernfonden [FO2021-0333]
- VR-M [2019-01254]
- international VR postdoc [2020-06365]
- Swedish Research Council [2019-01254] Funding Source: Swedish Research Council
This article presents a new data-driven model for accurately describing the features of excitatory postsynaptic currents in mouse striatum. Compared to traditional methods, this new model uses more time constants and provides a more accurate description, while maintaining low computational costs.
The majority of excitatory synapses in the brain uses glutamate as neurotransmitter, and the synaptic transmission is primarily mediated by AMPA and NMDA receptors in postsynaptic neurons. Here, we present data-driven models of the postsynaptic currents of these receptors in excitatory synapses in mouse striatum. It is common to fit two decay time constants to the decay phases of the current profiles but then compute a single weighted mean time constant to describe them. We have shown that this approach does not lead to an improvement in the fitting, and, hence, we present a new model based on the use of both the fast and slow time constants and a numerical calculation of the peak time using Newton's method. Our framework allows for a more accurate description of the current profiles without needing extra data and without overburdening the comptuational costs. The user-friendliness of the method, here implemented in Python, makes it easily applicable to other data sets.
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