Computational Neuroscience, a relatively recent discipline within the broader field of neuroscience, has emerged as crucially important for furthering our understanding of brain function and translating this knowledge into technological applications.

Boston University faculty have made many foundational contributions to computational neuroscience, and BU currently has one of the largest and most varied computational neuroscience faculties in the world.

Computational Neuroscience Curriculum & Advisory Committee: our computational specialization is managed by a unique team of leading neuroscientists (Uri Eden, Marc Howard, Nancy Kopell, Mark Kramer, Sam Ling, David Somers, Emily Stephen (Chair), and Arash Yazdanbakhsh) that work closely with the GPN Director to update and expand BU CompNeuro opportunities for our students.

Topics of study include neural network modeling, neural dynamics, sensory, motor, and cognitive modeling, statistical modeling, sensory prosthesis, brain-machine interfaces, neuroinformatics, neuromorphic engineering, and robotics. Coursework is chosen from the wide array of computational and neuroscience courses offered by the many departments and programs of the main Boston University campus and the BU School of Medicine. Students pursue their thesis interests in laboratories across the University and have the opportunity to combine hands on experimental research with highly sophisticated computational analysis.

BU CompNeuro Faculty

	David Boas Neuro-photonics, biomedical optics, neuro-vascular coupling		Nancy Kopell Neural dynamics; rhythmic behavior in neural networks
	Chandramouli Chandrasekaran Electrophysiology, analytical techniques, brain computer interface and computational modeling to study the neural basis of goal-directed behavior		Sam Ling Visual processing, attention, learning and awareness
	Rachel Denison Behavioral Measurements (Psychophysics, Eye Tracking), Neural Measurements (FMRI, EEG/MEG), and computational modeling		Joe McGuire Neural representation of subjective value, decision making, weighing cost of individual effort
	Brian Depasquale Artificial and biological intelligence, machine learning		Gabriel Ocker Theoretical neuroscience, studying structure-function relations in neuronal network models
	Michael Economo Neural circuits distributed across the brain that control movement		Tyler Perrachione Developmental disorders of language, social auditory perception, brain bases of complex auditory processing(including speech and voice perception)
	Uri Eden Mathematical and statistical modeling of neural spiking activity		Kamal Sen Neural coding of natural sounds; hierarchical auditory processing
	Jeff Gavornik Cortical circuits, synaptic plasticity as the basis of learning and memory, the neural representation and processing of time		David Somers Visual perception and cognition; neuroimaging; neural modeling
	Frank Guenther Speech neuroscience; neural prosthesis; neuroimaging		Emily P. Stephen Statistical Neuroscience
	Xue Han  Neurotechnology, optogenetics, neural prosthetics		Cara Stepp Sensorimotor function disorders
	Michael Hasselmo Memory-guided behavior; role of oscillations in cortical function		Lucia Vaina Computational models of vision; neuroimaging
	Marc Howard Cognition and neural representation of time and space		John White Information processing and cortical electrical activity; biomedical devices
	Mark Kramer Neural dynamics; neural rhythms in normal and diseased brain		Arash Yazdanbakhsh Human vision and its modeling; human electrophysiology and psychophysics
	Mark Kon Machine learning and bioinformatics; neural network theory