University of Utah
To create intelligence, the key step is to understand how the brain generates our behavior through the integration of various internal and external factors including sensory, motor, and cognitive information. Such an understanding heavily relies on an ability to quantitatively describe how information about those factors is represented in neuronal responses (encoding), and how our behavior is read out from those responses (decoding). This is often difficult to achieve due to the complexities of neural data and limitations of computational models. The overarching goal in our lab is to overcome these challenges via developing mathematically-principled, and biologically-plausible modeling frameworks capable of describing sparse, dynamic, high-dimensional neuronal data, and to employ these methods to provide new insights into the neural basis of cognition and action. Our research employs an integrative approach which involves a variety of techniques including array electrophysiological recording, pharmacological manipulation, computational modeling, statistical learning and inference, and psychophysical experiments in awake behaving nonhuman primates as well as human subjects. In this talk, we specifically focus on a research in our lab to elucidate the neural computations and circuit mechanisms underlying dynamic vision in order to discover the algorithms by which the brain actively constructs a vivid perception based on a distorted visual representation, three times a second, during our rapid eye movements
Dr. Neda Nategh is currently an Assistant Professor of Electrical and Computer Engineering, and Research Assistant Professor of Ophthalmology and Visual Sciences with the University of Utah. She received her Ph.D. in electrical engineering, her M.Sc. in electrical engineering, and her M.Sc. in statistics, all from Stanford University, and her B.Sc. in electrical engineering from Sharif University of Technology. She also holds a certificate in Biophysics and Computation in Neurons and Networks from the Neuroscience Institute at Princeton University.