Cognitive computational neuroscience: developing neuro-AI models to simulate human cognition

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Cognitive computational neuroscience represents a rapidly evolving interdisciplinary field that merges principles from neuroscience, psychology, and artificial intelligence. By developing computational models that simulate human cognition, researchers can probe the underlying neural mechanisms responsible for complex mental functions such as perception, memory, reasoning, and decision-making. Recent advances in neuroimaging, big data analytics, and machine learning offer unprecedented opportunities for the development of sophisticated Neuro-AI models. These models not only provide insight into fundamental brain processes, but also serve as testbeds for theories of cognition, driving a deeper understanding of both biological and artificial intelligence.



Despite significant progress, a major challenge persists in creating Neuro-AI models that faithfully capture the intricacies of human cognition. Current models often struggle to integrate biological plausibility with computational power, and there remains a gap between the explanatory depth of neuroscientific data and the abstract architectures of AI. This Research Topic aims to address these challenges by bringing together innovative research that advances the development and validation of computational models inspired by human cognitive processes. We seek contributions that bridge the divide between experimental neuroscience and computational modeling, foster the translation of neuroscientific findings into AI architectures, and explore how such models can be used to simulate, predict, or even enhance cognitive functions. Achieving these goals will require collaborative, interdisciplinary efforts and the application of novel computational tools, validation strategies, and experimental paradigms.



We welcome submissions including original research, reviews, methods, and perspective articles addressing, but not limited to, the following themes:

• Development of neurobiologically inspired AI and computational models of cognition

• Integrating neuroscientific data into machine learning architectures

• Validation and benchmarking of Neuro-AI models against human or animal data

• Comparative studies of biological and artificial learning systems

• Applications of Neuro-AI models to simulate cognitive functions such as attention, memory, or reasoning

• Critical perspectives on the limitations and future directions of cognitively inspired computational neuroscience

• Methodological advances in model interpretability, scalability, and biological plausibility