Ph. D. Program in Neuroscience at Princeton

Ph.D. in Neuroscience at Princeton University : Application deadline Dec 1.

Ph.D. program in Neuroscience ( http://neuroscience.princeton.edu/PhD ) within the Princeton Neuroscience Institute ( http://neuroscience.princeton.edu ). Fall 2010 will see the second generation of students enrolling in this relatively new Ph.D. program. Nine spectacular Ph.D. students started with us in 2009, and we are again seeking the most highly motivated and creative students.

Innovative coursework. A key component of our Ph.D. is year-long core course, taken in the first year and inspired by Woods Hole-style advanced courses. Students in this core course learn through a combination of lectures and first-hand experimental experience using modern, advanced methods. All students, regardless of previous experience, perform their own experiments. From single neurons and patch clamp, to ChR expression and activation, to in vivo electrophysiology in behaving animals, to computational modeling, to human neurophysiology and functional MRI, this course guides and teaches students about the brain as they learn to design, perform, analyze, and critique their own experiments.

Quantitative and Computational Neuroscience track. We strongly encourage students with training in quantitative fields such as physics, mathematics, computer science, or engineering to apply to our PhD program. Research in quantitative approaches to the Life Sciences is particularly strong at Princeton University, including molecular biology, neuroscience, evolutionary biology, and psychology. A Quantitative and Computational Neuroscience (QCN) track exists within our neuroscience Ph.D. It teaches students with a quantitative background about neuroscience problems to which they can apply their quantitative skills. The QCN track also serves students with a biology background who wish to acquire further training in quantitative tools for the biological sciences.

Faculty and research interests.

Michael Berry : Neural computation in the retina
William Bialek : Interface between physics and biology
Matthew Botvinick : Neural foundations of human behavior
Lisa Boulanger : Neuronal functions of immune molecules
Carlos Brody : Quantitative and behavioral neurophysiology
Jonathan Cohen : Neural bases of cognitive control
Jonathan Eggenschwiler : Mouse neural development
Lynn Enquist : Neurovirology
Liz Gavis : Neural development in Drosophila
Alan Gelperin : Learning, memory and olfaction
Asif Ghazanfar : Neurobiology of primate social agents
Elizabeth Gould : Neurogenesis and hippocampal function
Michael Graziano : Sensorimotor integration
Charles Gross : Functions of the cerebral cortex in behavior
Uri Hasson : Temporal scales of neural processing
Bartley Hoebel : Behavioral neuroscience
Philip Holmes : Mathematical modeling
Barry Jacobs : Brain monoamine neurotransmitters
Sabine Kastner : Neural mechanisms for visual perception
Mala Murthy : Neurophysiology of perception in Drosophila
Coleen Murphy : Molecular mechanisms of aging
Yael Niv : Reinforcement learning and decision making
Ken Norman : Neural bases of episodic memory
Daniel Osherson : How does the brain reason?
David Tank : Neural circuit dynamics
Samuel Wang : Dynamics and learning in neural circuits