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 Daniel Bullock).
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||Mark Kramer Neural dynamics; neural rhythms in normal and diseased brain
|Jason Bohland Speech neuroscience; neuroimaging; neuroinformatic||Mark Kon Machine
learning and bioinformatics; neural network theory
|Daniel Bullock Neural modeling of voluntary action and reinforcement learning||Eric Kolaczyk Statistical analysis of network-indexed data; biological networks modeling and data analysis|
|Gail Carpenter Neural networks; pattern recognition; neuromorphic technology||Nancy Kopell Neural dynamics; rhythmic behavior in neural networks|
|Michael Cohen Speech processing; measurement theory; cardiovascular modeling||Sam Ling Visual processing, attention, learning and awareness|
|Stephen Colburn Binaural hearing; neural modeling; hearing impairments||Joe McGuire Neural representation of subjective value, decision making, weighing cost of individual effort|
|Uri Eden Mathematical and statistical modeling of neural spiking activity||Hamid Nawab Signal processing of neural activity; auditory scene analysis|
|Timothy Gardner Songbird neural circuit development; neural recording technology||Kamal Sen Natural sound encoding; auditory plasticity; birdsong|
|Jeff Gavornik Cortical circuits, synaptic plasticity as the basis of learning and memory, the neural representation and processing of time||Eric Schwartz Computational neuroscience; machine vision; neuroanatomy|
|Stephen Grossberg Neural modeling of vision; speech; cognition; emotion; motor control; navigation; mental disorders||David Somers Visual perception and cognition; neuroimaging; neural modeling|
|Frank Guenther Speech neuroscience; neural prosthesis; neuroimaging||Cara Stepp Sensorimotor function disorders|
|Xue Han Neurotechnology, optogenetics, neural prosthetics||Malvin Teich Biosignal analysis; audition; vision; biological imaging|
|Michael Hasselmo Memory-guided behavior; role of oscillations in cortical function||Jason Tourville Speech motor control; neuroimaging; neuroanatomy|
|Marc Howard Cognition and neural representation of time and space||Lucia Vaina Computational models of vision; neuroimaging|
|Allyn Hubbard Auditory physiology; VLSI; neurocomputing||Arash Yazdanbakhsh Human vision and its modeling; human electrophysiology and psychophysics|