A Practical Guide to Artificial Intelligence in Neuroscience
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Beschreibung
This book is a practical, application-focused guide to artificial intelligence and machine learning in neuroscience, designed to help readers use these methods without requiring advanced mathematics or deep technical prerequisites. It distinguishes itself by pairing each method with a neuroscience example and ready-to-use code, making it especially useful for students and researchers who want concise, applied instruction rather than a purely theoretical treatment.
A Practical Guide to Artificial Intelligence in Neuroscience covers the major tools used in contemporary data-driven neuroscience, from regression and classification to clustering, dimensionality reduction, neural networks, deep learning, fuzzy systems, and natural language processing. It shows how to move from raw brain data to clinically meaningful predictions and insights, without requiring advanced mathematics or computer science training. The chapters are organized to move from classical supervised methods through unsupervised learning and into advanced neural approaches, giving readers a coherent path from foundational statistical modeling to modern AI applications in brain and health research.
Designed for time-pressed graduate and undergraduate students in medicine, psychology, and health-related fields, as well as practicing neuroscientists, A Practical Guide to Artificial Intelligence in Neuroscience bridges the gap between powerful modern AI tools and the real-world questions neuroscientists and medical researchers need to answer.
This book is a practical, application-focused guide to artificial intelligence and machine learning in neuroscience, designed to help readers use these methods without requiring advanced mathematics or deep technical prerequisites. It distinguishes itself by pairing each method with a neuroscience example and ready-to-use code, making it especially useful for students and researchers who want concise, applied instruction rather than a purely theoretical treatment.
A Practical Guide to Artificial Intelligence in Neuroscience covers the major tools used in contemporary data-driven neuroscience, from regression and classification to clustering, dimensionality reduction, neural networks, deep learning, fuzzy systems, and natural language processing. It shows how to move from raw brain data to clinically meaningful predictions and insights, without requiring advanced mathematics or computer science training. The chapters are organized to move from classical supervised methods through unsupervised learning and into advanced neural approaches, giving readers a coherent path from foundational statistical modeling to modern AI applications in brain and health research.
Designed for time-pressed graduate and undergraduate students in medicine, psychology, and health-related fields, as well as practicing neuroscientists, A Practical Guide to Artificial Intelligence in Neuroscience bridges the gap between powerful modern AI tools and the real-world questions neuroscientists and medical researchers need to answer.Offers practical AI and machine learning methods for neuroscience students, researchers, and practitioners Includes ready-to-use computer code for every machine learning method applied directly to neuroscience research No advanced math required: Apply complex AI through clear, practical explanations for medical and health researchers
Autor*in
Nader Fallah
Themen in »A Practical Guide to Artificial Intelligence in Neuroscience«
Computational Neuroscience Methods Neuroimaging Machine Learning Deep Learning for Brain Imaging Statistical Analysis Brain Data NeuroAI and Computational Psychiatry Machine Learning in Neuroscience