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We study approaches in the computational modeling of biological neural systems by combining systems-level neuroscience, mathematical modeling, and computer simulation techniques.

The aim is to bring together anatomical, physiological, and psychological data by modeling techniques. We also study the psychological, biological, mathematical and computational foundations of visual perception by combining  computer simulation, 3D visual psychophysics, and eye tracking. One of our key questions relates to the presence or lack of correlation between certain percept changes and eye movements.

By conducting sensory-motor experiments and neural modeling, we also approach clinical conditions such as Parkinson’s  diseases (PD) and the oculomotor correlates of motor and sensory conditions in PD. Computational Neuroscience & Vision Lab is particularly interested in topics related to figure-ground segregation, scene segmentation, and motion perception, for which we consider all of the above approaches.

Various methods of data analysis are the integral part of the above approaches ranging from statistical and machine learning based classification and clustering algorithms on the data stemming from the psychophysical and eye tracking experiments.

Computational Neuroscience & Vision Lab is in Psychological & Brain Sciences Department and is affiliated with Graduate Program for Neuroscience (GPN), Center for Systems Neuroscience (CSN), and Center for Research in Sensory Communications and Neural Technology (CReSCNT).