A Pipeline based on Differential Evolution for Tuning Parameters of Synaptic Dynamics Models

Integrating the modulatory properties of Synaptic Dynamics (SD) into Spiking Neural Networks (SNNs) can enhance their computational capabilities.For improving this integration process, this paper presents a pipeline based on Differential Evolution to tune parameters of SD models.Using reference signals from in vitro experiments, parameters of two models are tuned as study cases: the Tsodyks-Markram and the Modified Stochastic Synaptic Model.The pipeline has an average success rate of 75% and 80% respectively.The outcome is a distribution of parameters for each model, which can be considered as prior knowledge to facilitate the integration of SD models into SNNs.