Understanding the complex calcium dynamics in astrocytes is pivotal for deciphering their role in neural information processing. Current methodologies, predominantly based on event detection and subsequent clustering, fail to capture the intricate spatial-temporal patterns of astrocytic calcium. We propose a novel, iterative approach to decode these dynamics through simulation. The method consists of four main steps: 1) extraction of astrocytic events from calcium imaging data, 2) delineation of astrocyte footprints using Convolutional Neural Networks (CNNs), 3) event proposal generation through Recurrent Neural Networks (RNNs), and 4) comparison of simulated and real-life recordings for parameter refinement. Our model aims to iteratively refine itself until it closely matches empirical data, providing unprecedented insights into decoding the "calcium code" and potentially revolutionizing our understanding of astrocyte-neuron interactions.