Abstract
Understanding how neuronal circuits generate complex activity patterns and perform computations remains a significant challenge in neuroscience. In vitro neuronal models provide controlled environments to investigate brain microcircuits, their responses to stimuli, and dysfunctions in pathological conditions. While invaluable for direct observation and manipulation, these experiments are also resource-intensive and raise ethical concerns, particularly when involving human-derived neurons. In silico models offer a cost-effective, scalable complementary alternative. They integrate multi-scale data, enabling high-throughput investigations and the exploration of mechanisms that may be beyond the reach of experimental methods. These computational approaches support hypothesis generation, data interpretation, and theoretical insight. When combined with in vitro studies, they create a synergistic framework that advances our understanding of neuronal function and dysfunction in ways neither method could achieve alone. This review examines computational models developed since 2000 to support in vitro neuronal investigations, with a focus on their contributions to understanding network dynamics. This includes topics such as neuronal activity, stem-cell-derived neurons, network topology, and metabolism. We highlight key applications, from predicting mechanisms of neuropathy to exploring network learning and memory. We offer an overview of a corner problem for the development of computational models, that is parameter estimation, and discuss implementation strategies emphasizing accessibility through public repositories. By synthesizing these developments, this review aims to inspire new approaches in computational neuroscience, advancing the study of brain function and dysfunction.