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 Computational Neuroscience
How does the brain compute? This is a fundamental question of neuroscience -- it's related to every function of the brain, since almost all tasks require computation. Imagine the many computations required to move your hand and grasp an object...or parse a visual scene into objects.
The computational approach to neuroscience involves computer simulations and modeling, and the goals are generally one or more of the following: 1) to conduct in silico tests of ideas and hypotheses; 2) to get a better understanding of experimental data, particularly data coming from diverse methodologies; and 3) to generate new and testable hypotheses to guide future experiments.
Labs at Penn that have computational neuroscience projects:
Benjamin T. Backus, PhD Psychophysics; computational modeling; evoked potentials, fMRI
David H Brainard, PhD Visual perception and its neural mechanisms; digital image processing.
Gershon Buchsbaum, PhD Visual signal processing and image coding, modeling of retinal and visual system architecture and function, computational neuroscience and neural networks
Nader Engheta, PhD Biologically inspired polarization imaging and applications, electromagnetics, optics
Nabil H. Farhat, PhD The focus of my research is in Corticonics where I am applying concepts and tools from nonlinear dynamics, bifurcation theory, self-organized criticality, complexity, and chaos to the modeling and study of the cortex. In corticonics (echoing electronics) I am concerned with developing a dynamical approach to understanding the cortex and its collective codes for information processing.
George L. Gerstein, PhD Representationof information in auditory and visual systems, particularly with reference to function of assemblies of neurons; models of neuronal networks
Joshua Gold, PhD How the brain forms decisions about sensory stimuli: What are the underlying neural computations? Where are the circuits that perform these computations? How are these circuits shaped by experience?
Alan Gottschalk, MD/PhD
Theory of neural information processing and control; self-organization
and adaptation of networks which perform information processing and
control; nonlinear dynamics of neural systems; quantification of the
clinical correlates of these processes
Michael J. Kahana, PhD Human memory and its neural mechanisms; Brain Oscillations
Max B. Kelz, MD, PhD EEG/EMG study of behavioral state including spectral analysis and chaos theory entropy analysis
Daniel D. Lee, PhD Computational neuroscience
machine learning, biologically-inspired artificial sensorimotor systems
Richard Murray, PhD Visual
psychophysics, spatial vision, perceptual organization, visual attention;
natural image statistics in relation to shape from shading and perceptual
organization; signal detection theory, ideal decision theory, multiple
spatial frequency channel theory
Robert G. Smith, PhD Understanding how the structure and biophysical properties of a neuron influence the signal processing function of the surrounding neural circuit
Santosh S. Venkatesh, PhD Neural networks; statistical pattern recognition; computational learning theory; information and complexity theory
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