Computational models of memory and attention: Advancing mechanistic understanding of brain with Artificial Intelligence and beyond

Human memory and attention are core cognitive functions that shape perception, learning, and decision-making. And whilst decades of research have provided valuable empirical insights, the underlying neural mechanisms remain an open challenge in neuroscience. Computational neuroscience offers powerful tools for modeling these processes, and recent advances in artificial intelligence have created new opportunities to develop models that capture the complexity and dynamics of biological cognition.By simulating brain function, computational models not only replicate behavioral and neural phenomena but also generate testable hypotheses, unify diverse findings, and move beyond description toward mechanistic understanding. However, despite their impressive performance, many AI models lack clear links to biological plausibility. This Research Topic aims to bridge that gap by highlighting state-of-the-art computational and AI-driven approaches that reveal how memory is formed, consolidated, and retrieved, and how attention selectively modulates information processing.We welcome interdisciplinary contributions that combine theory, modeling, and empirical data to deepen our understanding of these processes. Submissions may include, but are not limited to: - Neural network models of working memory, episodic memory, and semantic memory. - Computational frameworks for selective attention, attentional control, and saliency detection. - Models integrating AI with neurophysiological data (e.g., fMRI, EEG, single-unit recordings). - Computational theories of learning, memory consolidation, and forgetting. - Models of dynamic interactions between memory systems and attentional processes. - Machine learning approaches for predicting cognitive performance. - Novel computational architectures inspired by brain systems for memory and attention. - Computational insights into deficits in memory and attention in neurological or psychiatric conditions.By encouraging dialogue across neuroscience, AI, and cognitive science, this Research Topic looks to uncover general principles governing memory and attention and to advance mechanistic models that link mind, brain, and computation. Topic editors: Amedeo D'Angiulli, Robin Chhabra Source: https://www.frontiersin.org/research-topics/77882/computational-models-of-memory-and-attention-advancing-mechanistic-understanding-of-brain-with-artificial-intelligence-and-beyond