Computational neuroscience is a rapidly evolving field that seeks to understand neural dynamics and cognitive processes through mathematical and computational approaches. With advances in neuroimaging, electrophysiological techniques, and machine learning, researchers are now able to explore the intricate relationships between neuronal activity and complex cognitive functions at ever finer spatial and temporal scales. However, despite significant technological progress, deciphering the full mechanisms underlying how the brain generates cognition from dynamic neural activity remains a major scientific challenge. Key questions persist regarding how different scales of neural organization—from single neurons to large-scale brain networks—interact to support learning, memory, perception, and decision-making.

Recent studies have pushed the boundaries of modeling, from biophysically detailed simulations to data-driven neural network architectures, and these approaches have yielded new insights into the neural basis of cognitive phenomena. For example, advances in recurrent neural networks and dynamical systems theory have facilitated more accurate predictions of brain activity and behavioral outcomes. Nonetheless, integrating these computational models to bridge the gap between microscopic neural mechanisms and macroscopic cognitive functions is still in its infancy, hindered by limitations in data quality, model interpretability, and the translation of computational outputs back into physiological relevance.

This Research Topic aims to advance our understanding of the interplay between neural dynamics and cognition by highlighting innovative computational models and approaches. We seek contributions that address open questions regarding the mapping of neural activity to cognitive processes, validation of models against biological data, and the potential for such models to inform new theories of brain function. The objective is to encourage interdisciplinary discourse and foster collaborations that will propel the field toward unified frameworks for brain-cognition research.

To gather further insights in the domain of advanced computational models for neural dynamics and cognition, we welcome articles addressing, but not limited to, the following themes:

o Mechanistic and data-driven models of neural networks underlying cognition

o Integration of multi-scale neural dynamics with cognitive theories

o Machine learning and artificial intelligence methods in brain modeling

o Validation and interpretability of computational models using experimental data

o Theoretical frameworks linking neuronal activity to cognitive function

o Emerging technologies and tools for large-scale brain modeling

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Advanced Computational Models for Neural Dynamics and Cognition