Provides comprehensive guidance on harnessing artificial intelligence for neuroscience research and clinical applications The rapid development of artificial intelligence (AI) has created new opportunities for advancing the study of the brain. While recent scholarship has focused on how neuroscience can inform the design of AI systems, there is a growing need for resources that demonstrate how AI can be applied to support neuroscience research and practice. Artificial Intelligence in Neuroscience offers a detailed introduction to AI technologies and their transformative potential for fields ranging from neuroimaging and genetics to mental healthcare and neuro-oncology. Structured around four major areas, the book begins by exploring the shared history of AI and neuroscience, from single-neuron modeling and action potentials to contemporary learning mechanisms. It then examines how AI can be used as a data analysis tool in genetics, proteomics, histology, cognition, and population health, before turning to clinical applications such as biologically plausible cognitive models, connectionist frameworks, and reinforcement learning. Additional chapters consider emerging applications, including robotics, drug screening, brain-computer interfaces, and language models. The volume concludes with a critical discussion of ethical and privacy issues, ensuring readers are equipped to navigate the responsibilities that accompany technological innovation. Wide in scope and filled with practical insights, Artificial Intelligence in Neuroscience: Explores the historical intersections between AI and neuroscience to contextualize current innovationsDemonstrates applications of AI in neuroimaging, genetics, and population health researchDetails clinical applications of AI models, including reinforcement learning and connectionist frameworksHighlights novel uses of AI in robotics, brain-computer interfaces, and drug discoveryIntegrates technical depth with applied case studies for both
