DL Applications in Neuroinformatics by Karthik Ramamurthy (.PDF)
File Size: 10 MB
Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives by Karthik Ramamurthy
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Overview: Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how Deep Learning revolutionizes neuroinformatics, covering the latest methods and applications of Deep Learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and Artificial Intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts. Deep Learning is a powerful tool that provides a flexible and robust computational framework. It is capable of handling high-dimensional and domain-specific datasets to address these challenges. Participating networks, such as artificial neural networks (ANNs), attempt to simulate the structure and functions of biological neurons. Early neural network models are capable of detecting patterns and relationships within data despite their limited learning depth. These characteristics make them applicable to neuroinformatics-related tasks. Today, Deep Learning models outperform most methodologies at finding subtle patterns in data. This unlocks potential in various domains such as disease prediction, brain region segmentation, and decrypting the neural signals.
Genre: Non-Fiction > Tech & Devices
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