BMC Neuroscience is calling for submissions to our Collection on cognitive neuroscience and AI. Creating artificial neural networks that can replicate the functions and pattern recognition abilities of the human brain remains one of the primary goals of AI development today. Likewise, as artificial intelligence grows more advanced, it has become a useful guide in helping us increase our understanding of how our own brains function. The two fields have become more and more reciprocal in recent years, and advancements in the fields of cognitive science and AI have never been more connected.
With this Collection, we aim to promote the integration of knowledge and advances in both fields, with the ultimate goal of advancing our understanding of human cognition and developing more sophisticated AI systems. The Collection will cover a range of topics that lie at the intersection of cognitive neuroscience and AI, including but not limited to: neural network models of cognition, deep learning algorithms for neuroimaging data, brain-computer interfaces, and cognitive architectures for artificial intelligence. The collection will publish original research that contributes to the advancement of our understanding of the relationship between human cognition and artificial intelligence.
We welcome submissions from researchers and educators in the fields of neuroscience, artificial intelligence, psychology, and related disciplines, as well as professionals in the technology and pharmaceutical industries who are interested in the intersection of these fields. Topics of interest to this Collection include, but are not limited to:
• Using neural networks to model cognitive processes
• Neural network models of cognitive processing
• Deep learning algorithms for neuroimaging applications
• Cognitive architectures for developing intelligent AI systems
• Integration of neuroimaging techniques and AI algorithms for predicting treatment outcomes in neurological disorders
• Investigating the neural basis of attentional control using machine learning approaches
• Developing explainable AI models for understanding neural mechanisms of learning and memory
• Using cognitive neuroscience to guide the development of machine learning models for clinical decision-making
• Investigations into the effects of non-invasive brain stimulation on neural networks and its implications for AI and cognitive neuroscience
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