Data-Driven Model of Postsynaptic Currents Mediated by NMDA or AMPA Receptors in Striatal Neurons - Carannante, Ilaria; Johansson, Yvonne; Silberberg, Gilad; Hellgren Kotaleski, Jeanette (Frontiers in Computational Neuroscience, 2022)
The majority of excitatory synapses in the brain use 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. 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 computational costs. The raw data used was acquired ex vivo by recording from striatal neurons while activating different striatal inputs. The user-friendliness of the method, here implemented in Python, makes it easily applicable to other data sets.