Computational Neuroscience

Computational Neuroscience is an approach to understand brain function by modeling neuronal control spanning from molecular and cellular levels to system levels. By simulating and modeling brain function, computational neuroscientist aim to understand how various neural networks compute information. Research in computational neuroscience has enhanced our understanding of neurological systems with the most prominent example being the Nobel Prize winning work of Hodgkin and Huxley in understanding action potentials. This is an area of strength for our graduate program. Most of the laboratories at the University of Minnesota with an expertise in computational analysis of the nervous system also have fully developed experimental programs to complement their computational modeling. Many of the areas of focus in these laboratories are in the domains of behavioral and cognitive neuroscience. Courses in Computational Neuroscience are taught yearly, including both cellular models and systems and information processing.

Areas of research include: basic mechanisms of epilepsy and the complex dynamics of neuronal integration;  modeling intrinsic cortical circuitry; motor control; pattern recognition and decision making; dynamics of neural ensembles during decision-making; brain-machine interface; integration of visual, haptic, and motor information during the perception-action cycle; protein structure as it pertains to muscle function; neural communication as visualized with electroencephalography (EEG), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), and other state-of-the-art imaging modalities.


Kathryn Cullen
Damien Fair
Paloma Gonzalez-Bellido
Arif Hamid
Suma Jacob
Thomas Naselaris
Jessica Nielson
Michael-Paul Schallmo
Martha Streng
Brenden Tervo-Clemmens
Anna Zilverstand
Jan Zimmermann