Data Analysis in Neuroscience

The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

In recent years, technological advances have resulted in the rapid development of a new exciting research direction –- the interdisciplinary use of sensors for data collection, systems analysis, and monitoring. Application areas include environmental monitoring, military surveillance, computational neuroscience, seismic detection, and many more. Among the research areas considered, sensor networks represent a very active area, as well as work on problems such as sensor networks, sensor network localization, and network design. They are also related to optimization problems that have multiple applications and require interdisciplinary techniques from several fields. Additionally, neuroscientists use sensors to collect data during tests which record brain activity. These highly calibrated sensors require precision engineering techniques play an important role in analyzing and integrating large datasets.

Supercharge your business with context-aware AI controllers, adaptive agents, multimodal reasoning functionality, neuroscientific memory systems, and flexible handler mechanisms that integrate the emerging generative AI models. Get with your book: PDF copy, AI Assistant, and Next-Gen Reader free.

by Walter J Scheirer (Author), Gerard Medioni (Editor), Sven Dickinson (Editor)

This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functional architecture, that is, the highly specific organization of its neural connections.

This book analyzes the application of computer science and artificial intelligence (AI) techniques in the semantics’ analysis for linguistics, classical studies, and philosophy. Similar techniques can be implemented to incorporate the fields of education, psychology, humanities, law, maritime, data science and business intelligence. The book is suitable for the broader audience interested in the emerging scientific field of formal and Natural Language Processing (NLP).