Simulating developmental neurobiology is a growing area of research that uses computational models to understand how the brain develops from early embryonic stages through adulthood. Brain development involves complex, dynamic processes—including neural proliferation, migration, differentiation, synaptogenesis, and pruning—that are regulated by both genetic and environmental factors. Traditional experimental approaches have provided valuable insights but are often limited in their ability to capture the full complexity and temporal progression of neural development. Computational modeling offers a powerful complementary approach, allowing researchers to simulate developmental processes across scales—from molecular signaling pathways to large-scale brain networks. These models can help uncover how early disruptions in development may lead to neurodevelopmental disorders such as autism, ADHD, and schizophrenia. With recent advances in data collection, imaging, and machine learning, simulation-based studies are becoming increasingly important for testing hypotheses, integrating diverse datasets, and generating predictive frameworks in developmental neuroscience.

Understanding brain development remains a major challenge in neuroscience. While experiments have identified key developmental stages and mechanisms, integrating this knowledge across scales and timeframes is difficult. Complex, dynamic processes—like cell proliferation, migration, synaptic pruning, and plasticity—interact nonlinearly. Disruptions in these processes can lead to neurodevelopmental disorders, yet predicting how such changes lead to pathology remains elusive. Computational modeling provides a promising framework for addressing this complexity by simulating developmental processes and testing how molecular, cellular, and network-level dynamics give rise to brain structure and function. Recent advances in imaging technologies, single-cell sequencing, and artificial intelligence have made it possible to build more accurate, data-driven models of brain development. The goal of this Research Topic is to bring together studies that use computational approaches to simulate aspects of neurodevelopment. We invite work that integrates biological data, develops multiscale models, or explores how deviations in development contribute to disorders. This topic seeks to advance our understanding of brain maturation and support the development of predictive, mechanistic insights into neurodevelopmental trajectories.

This Research Topic invites original research, reviews, and methodological papers focused on computational modeling of brain development. We welcome contributions that simulate key neurodevelopmental processes such as neurogenesis, cell migration, synapse formation and pruning, and activity-dependent plasticity. Studies may address development across various spatial and temporal scales—from molecular signaling and cellular interactions to the emergence of large-scale brain networks. We are particularly interested in models that integrate biological data, explore mechanisms underlying typical and atypical development, or predict outcomes related to neurodevelopmental disorders such as autism, ADHD, or epilepsy. Submissions that utilize machine learning, agent-based modeling, or systems biology approaches to simulate developmental trajectories are encouraged. Interdisciplinary studies that bridge developmental biology, neuroscience, and computational sciences are especially welcome. Overall, the goal is to highlight how simulations can deepen our understanding of brain maturation and its disruptions, and support the development of predictive, mechanistic frameworks in developmental neurobiology.

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Simulating Developmental Neurobiology