Artificial Neural Networks (ANNs) and Spiking Neural Networks (SNNs) represent two distinct but complementary approaches to information processing. ANNs, with their continuous activation functions, have been widely successful in tasks requiring nonlinear mapping, while SNNs offer a biologically plausible and energy-efficient alternative, leveraging their discrete spike-based activity and spatio-temporal dynamics. This paper aims to decouple and analyze the contributions of spatio-temporal coding in these models. By examining how information is distributed across spatial, temporal and activation domains, we introduce a novel mutual-information-based measure, neural activity exploitation rate, to quantify the efficiency of information utilization across these three dimensions. Our findings highlight the key differences in how ANNs and SNNs exploit neural activities and propose an incremental, evolutionary framework to evaluate the transition between these two modeling paradigms.