Machine Learning for Neuroscience

A Systematic Approach
1. Auflage 2023
ISBN: 978-1-032-13672-1
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 306 Seiten, Format (B × H): 162 mm x 241 mm, Gewicht: 565 g

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.

Zielgruppe

Academic

Autoren/Hrsg.

Easttom, Chuck

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Neurowissenschaften, Kognitionswissenschaft
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

1. Basic Linear Algebra. 2. Overview of Statistics. 3. Introduction to Python Programming. 4. More with Python. 5. General Neuroanatomy and physiology. 6. Cellular neuroscience. 7. Neurological disorders. 8. Introduction to Computational Neuroscience. 9. Overview of machine learning. 10. Artificial Neural Networks. 11. More with ANN. 12. K Means Clustering. 13. K Nearest Neighbors. 14. Self Organizing Maps.

Über Autor(innen)

Dr. Chuck Easttom is the author of 32 books. He is an inventor with 22 computer science patents. He holds a Doctor of Science in cybersecurity, a Ph.D. in Nanotechnology, and a Ph.D. in computer science as well as three master’s degrees (one in applied computer science, one in education, and one in systems engineering). He is a senior member of both the IEEE and the ACM. He is also a Distinguished Speaker of the ACM and a Distinguished Visitor of the IEEE. He has been active in the IEEE Brain Computer Interface Standards and is a member of the IEEE Engineering in Medicine and Biology Society.