Machine and Deep Learning Methods for Neuroscience

Artificial intelligence (AI) techniques such as machine learning and deep learning, which can handle vast volumes of complex data, have a substantial impact on real-world problem solving. Artificial intelligence has also been used successfully in biomedical applications.  A particularly active area of research is the utilization of biomedical data and images for AI-based clinical decision making, diagnostic studies, and medical knowledge engineering in neuroscience problems. Biomedical signals and images obtained through MRI, computed tomography, X-rays, pathology, microscopy, and EEG are frequently used in diagnostic-oriented studies, such as those investigating various types of neurological disorders (e.g., Alzheimer’s dementia, Parkinson’s disease, ALS, Multiple Sclerosis, Attention Deficit Hyperactivity Disorder, etc.). The development of deep learning and machine learning algorithms for application in neuroscience is currently a popular field of study due to their ability to solve a wide range of problems.

This special session will present cutting-edge research on machine and deep learning approaches applied to brain anatomical and functional data.

Researchers are encouraged to submit their original contributions on related topics at:


Izmir Katip Celebi University
Department of Biomedical Engineering

Brain Research Institute
University of Zurich
Zurich, Switzerland

Devrim UNAY
Izmir Democracy University
Department of Electrical and Electronics Engineering

Aydin AKAN
Izmir University of Economics
Department of Electrical and Electronics Engineering