his course introduces students to the fundamental principles of neuroimaging methods that are used in basic and applied neuroscientific research. Topics include history of neuroimaging as well as an overview of major neuroimaging techniques, including magnetic resonance imaging (MRI), functional MRI, diffusion tensor imaging, positron emission tomography, functional near-infrared spectroscopy, electroencephalography, and magnetoencephalography. The course will also address structural and functional neuroanatomy, basic physical principles, experimental design, statistical analysis and specific methodological principles and limitations associated with each imaging technique, as well as neuroimaging applications in studying the normal and diseased brain.

Neuroscience has played a key role in the history of artificial intelligence (AI). The development of artificial neural networks was inspired by the knowledge gained from the study of brain functioning, with neuroscientists and psychologists, such as Donald Hebb, William McCulloch, and Geoff Hinton, contributing significantly to the establishment of the field. AI researchers aim to emulate human intelligence by building models and developing biologically-inspired architectures that can make decisions and solve problems in the same way that humans do. At the same time, artificial intelligence is increasingly used as a research tool in neuroscience to advance our understanding of how the human brain works and to accelerate neuroscience development. For example, by analyzing the massive amounts of experimental data on brain activity acquired using neuroimaging techniques, machine learning is used to uncover the patterns in brain activity and link them to specific cognitive and motor actions. This course reviews the fundamental ideas in computational neuroscience and connects the study of the brain to the concepts and research in artificial intelligence. The list of example topics includes neural coding, the biophysics of single neurons and neuron models, neural networks, biological and computational vision, adaptation and learning, machine learning, deep convolutional networks, memory, speech and language processing, and applications of computational neuroscience and artificial intelligence.