Neuroscience
Graduate Courses
NEU 501. Neuroscience
In this course, students will develop an understanding of neurobiology at several levels, from the physiology of individual neurons, through the functioning of neural circuits, and finally to the behavior of neural systems such as vision, motion, and memory. Topics covered include spatial orientation and sensory guidance, neuronal control of motor output, neuronal processing of sensory information, sensorimotor integration, neuromodulation, circadian rhythms and cellular mechanisms of learning and memory Furthermore, students will learn about artificial intelligence and machine learning approaches to creating computational models of the brain using artificial neural networks and deep learning. The class will be based on lectures accompanied by in-class activities and will include weekly discussion of papers from the scientific literature. The class will focus on a guiding theme, such as neurotransmitter systems, with emphasis on research of human neurological problems, such as schizophrenia, addiction, and neurodegenerative disorders.
NEU 502. Neural Plasticity
Neuronal connections strengthen and weaken with learning, memory, or other events; a phenomenon called synaptic plasticity. In this course, we explore the underlying biological, biophysical and biochemical changes responsible for plasticity. This course covers the structure and organization of neuronal connections, the neurotransmitter receptors that line these structures, the signaling pathways that are mediated in synapses, the mechanical processes that underlie protraction and retraction, and the pharmacological agents that stimulate or block these changes. Students are required to have had an undergraduate level course in biology and biochemistry.
NEU 503. Computational Neuroscience
Computational neuroscience explores the brain at many different levels, from single cell activity, to small local network computation, to the dynamics of large neuronal populations across the brain. This course will introduce students to a multifaceted array of approaches that span biology, physics, mathematics and computer science as well as facilitate the integration of modeling (on both the single molecule and neuron level) and quantitative techniques to investigate neural activity at these different levels. Where possible, this course has a tripartite organization. First, the theory is presented from a text or journal article. Second, students read and critique a paper that uses the technique. Finally, simulations and/or problem sets are assigned to fix the knowledge learned in the course. Pertinent examples will be drawn from research done by WPI students and faculty.
NEU 504. Advanced Psychophysiology
This course will provide an in-depth understanding of what psychophysiology is and the common methods used to understand psychophysiological responses. Common psychophysiological methods will be discussed in-depth, such as sympathetic and parasympathetic nervous system, facial electromyography, electroencephalography (EEG), respiration, blood pressure, pulse rate, skin temperature, electrodermal responses, cortisol, and other neuroendocrine monitoring methods. The social, cognitive, emotional, and motivational responses to different psychological events will be explored in detail. Computational methods will be described from the fields of artificial intelligence, machine learning, and mobile computing for capturing, processing and discovering patterns in physiological and behavioral data. In addition, the course will examine how biofeedback works in educational, clinical, and experimental settings. Students may not receive credit for both PSY 2502 and NEU 504.
NEU 505. Brain-Computer Interaction
This course will explore the current state of brain sensing and its application to human-computer interaction research. This course covers brain function, sensing technology, machine learning methods, and applications of brain-computer interfaces in various domains. This course aims for students to (1) obtain the background to conduct research in brain-computer interaction and human-computer interaction; (2) understand the literature in the field of brain sensing for human-computer interaction research; (2) understand the various tools used in brain sensing, with a focus on functional near-infrared spectroscopy (fNIRS) research; (3) understand the steps required to use real-time brain sensing data as input to an interactive system; (4) understand the domains and contexts in which brain-computer interfaces may be effective; (5) understand the open questions and challenges in brain-computer interaction research today.
NEU 5900. Graduate Internship in Neuroscience
Graduate internship is carried out in cooperation with a sponsor or industrial partner. It must be overseen by a faculty member affiliated with the Neuroscience Program. The internship will involve development and practice of technical and professional skills and knowledge relevant to different areas of Neuroscience. At the completion of the internship, the student will produce a written report, and will present their work to core and affiliated Neuroscience faculty and internship sponsors.
NEU 599. M.S. Thesis Research in Neuroscience
A Masters thesis in Neuroscience consists of a research and development project worth a minimum of 9 graduate credit hours advised by a faculty member affiliated with the Neuroscience Program. A thesis proposal must be approved by the Neuroscience Program Review Board and the students advisor before the student can register for more than three thesis credits. The student must satisfactorily complete a written thesis document and present the results to the Neuroscience faculty in a public presentation.