Imagine the brain as a bustling city, with messages swiftly passing between buildings. One of the key messengers is glutamate, a neurotransmitter that plays a crucial role in excitatory synapses. In this study, we dive into the intricate world of AMPA and NMDA receptors, the gatekeepers of synaptic transmission in striatal neurons. Previous approaches have attempted to fit the decay phases of postsynaptic currents by using a single weighted mean time constant. However, our data-driven models reveal that this method falls short. Instead, we propose a novel model that takes into account both fast and slow time constants and employs Newton’s method to calculate the peak time. Not only does our approach provide a more accurate representation of current profiles, but it also eliminates the need for extra data and reduces computational costs. We’ve implemented this user-friendly model in Python, making it accessible for researchers working with different data sets.

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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