Carannante Ilaria, Johansson Yvonne, Silberberg Gilad, Hellgren Kotaleski Jeanette
Science for Life Laboratory, KTH Royal Institute of Technology, Department of Computer Science, Stockholm, Sweden.
Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom.
Front Comput Neurosci. 2022 May 11;16:806086. doi: 10.3389/fncom.2022.806086. eCollection 2022.
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.
大脑中大多数兴奋性突触使用谷氨酸作为神经递质，突触传递主要由突触后神经元中的AMPA和NMDA受体介导。在此，我们展示了小鼠纹状体兴奋性突触中这些受体突触后电流的数据驱动模型。通常会为电流曲线的衰减阶段拟合两个衰减时间常数，然后计算一个加权平均时间常数来描述它们。我们已经表明，这种方法并不能改善拟合效果，因此，我们提出了一种基于同时使用快速和慢速时间常数以及使用牛顿法对峰值时间进行数值计算的新模型。我们的框架无需额外数据且不会增加计算成本，就能更准确地描述电流曲线。该方法在Python中实现，具有用户友好性，易于应用于其他数据集。
