Understanding how the brain computes
Washington University in St Louis is home to a vibrant community of researchers addressing a wide range of issues in Computational Neuroscience. As befitting the highly interdisciplinary nature of the field, these researchers are spread over a wide range of departments on both the Medical School and Danforth campuses. We hold regular seminars to discuss recent advances in the field, usually on Tuesdays at 4pm.
For PhD students Washington University in St Louis also offers a training pathway in Cognitive, Computational and Systems Neuroscience (CCSN)
Some related graduate programs include Neuroscience, Psychological and Brain Sciences, Biomedical Engineering, Systems Science and Mathematics, Computational and Data Sciences, Computational and Systems Biology, Imaging Science, Physics, Mathematics, Statistics.
September 20th 2022: Kaining Zhang
October 4th 2022: Dennis Barbour
May 3rd 2022: Barani Raman
Feb 7th 2022: ShiNung Ching
November 2nd 2021: Gaia Tavoni
October 5th: Brendan Juba
May 4th 2021: Yixin Chen
Related Faculty include:
- Martha Bagnall (Neuroscience), Neural basis of balance
- Dennis Barbour (Biomedical Engineering), Laboratory of Sensory Neuroscience and Neuroengineering
- Phil Bayly (Mechanical Engineering & Materials Science), Brain biomechanics
- Mikhail Berezin (Mallinckrodt Institute of Radiology), Novel optically-active probes
- Janine Bijsterbosch (Mallinckrodt Institute of Radiology), Personomics
- Todd Braver, (Psychological and Brain Sciences), Cognitive Control & Psychopathology Laboratory
- Bruce Carlson, (Biology), Sensory and Evolutionary Neuroscience
- Shantanu Chakrabartty, (Electrical and Systems Engineering), Unconventional analog computing techniques.
- Yixin Chen (Computer Science and Engineering), Machine Learning and Artificial Intelligence.
- ShiNung Ching (Electrical and Systems Engineering), Brain Dynamics and Control Research Group.
- Tom Foutz (Neurology), Brain stimulation technologies
- Roman Garnet (Computer Science and Engineering), Bayesian machine-learning methods for sequential decision making under uncertainty.
- Geoff Goodhill (Developmental Biology & Neuroscience), Computational, Systems and Developmental Neuroscience
- Ed Han (Neuroscience), Learning and memory in the hippocampus
- Keith Hengen (Biology), Network dynamics, robust computation, plasticity, & disease
- Erik Herzog (Biology), Circadian rhythms
- Tim Holy (Neuroscience), Neural basis of behavior
- Brendan Juba (Computer Science and Engineering), Artificial Intelligence, algorithms and computational complexity.
- Bek Kamilov (Computer Science and Engineering), Computational imaging
- Adam Kepecs (Neuroscience & Psychiatry), Neurobiology of decision-making and cognition
- Wouter Kool (Psychological & Brain Sciences), Cognitive control, decision making, and reinforcement learning
- Eric Leuthardt (Neurosurgery), Brain computer interfaces
- Chenyang Lu (Computer Science and Engineering), Clinical machine learning and mobile health
- John McCarthy (Mathematics), Mathematics in Neuroscience
- Ilya Monosov (Neuroscience), Neuronal basis of voluntary behavior
- Dan Moran (Biomedical Engineering & Neuroscience), How we can control objects with our minds.
- Alvitta Ottley (Computer Science and Engineering), Machine learning and medical decision-making
- Camillo Padoa-Schioppa (Neuroscience), Neurobiological mechanisms of economic choice
- Barani Raman (Biomedical Engineering), Systems Neuroscience and Neuromorphic Engineering
- Larry Snyder (Neuroscience), How is sensory spatial information transformed into commands for movement?
- Gaia Tavoni (Neuroscience), Statistical physics and Bayesian approaches to brain function.
- David Van Essen (Neuroscience), Brain mapping techniques to study cerebral cortex
- Yevgeniy Vorobeychik (Computer Science and Engineering), Adversarial machine learning and data mining, data-driven agent-based modeling.
- Ralf Wessel (Physics), Neurophysics, Biophysics
- Dmitriy Yablonskiy (Mallinckrodt Institute of Radiology), New MRI methods
- Jeff Zacks (Psychological & Brain Sciences), Dynamic Cognition