Computational Neuroscience

The Computational Neuroscience Advisory & Curriculum Committee

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. Here at BU our computational specialization is managed by a unique team of leading neuroscientists that make up our Advisory & Curriculum Committee (upper left: Nancy Kopell, Mark Kramer, Arash Yazdanbakhsh, and Marc Howard; lower left: David Somers, Sam Ling, and Uri Eden).

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

The BU Graduate Program for Neuroscience (GPN) offers a Computational Neuroscience PhD for graduate students who wish to pursue rigorous training in this exciting field.  While all GPN students have the opportunity to take coursework or conduct thesis research that is computationally based, formal studies in computational neuroscience are organized by the Computational Neuroscience Advisory & Curriculum Committee, see above.

The Computational Neuroscience curriculum supplements core neuroscience training with advanced training in a wide array of computational methods for (i) studying the nervous system and (ii) developing neuroscience-related technologies. 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.

Potential applicants to the Computational Neuroscience PhD specialization apply directly through the GPN applicant portal.

BU Computational Neuroscience Faculty

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