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Quantitative and Computational Neuroscience

Princeton recently competed successfully for a grant from the NIH to train Quantitative and Computational Neuroscientists -- researchers for the next generation, with sophisticated quantitative and computational skills that will allow them to attack the problem of how the brain works in novel and creative ways.

The new Program in Quantitative and Computational Neuroscience (QCN) brings together a number of researchers with a common interest in quantitative approaches to neuroscience, and consists of both an undergraduate and a graduate training component.

We are actively seeking highly motivated students that wish to join Princeton's quantitative biology community, specifically within the new QCN program.


How do our brains work? How do millions of individual neurons work together to give rise to behavior at the level of a whole organism?

Going from the small to the large, great strides have been made in understanding the biophysics of individual neurons, and how neurons interact in small assemblies. Going from the large to the small, cognitive neuroscience has made great progress in characterizing major brain areas and functions involved in higher-level mental processes, such as perception, memory, and attention. However, a fundamental gap remains between these "bottom-up" and "top-down" approaches. We still don’t know how neurons cooperate with each other to form the large-scale collective computations that underlie perception and behavior. 

One reason for the gap between the two approaches is the complexity of the interactions involved.

Efforts to understand such complex systems have benefited in other domains of science by the application of formal theoretical techniques and sophisticated methods for quantitative data analysis. Powerful methods are now being applied and developed for neuroscience.

Computers can also assist in managing complexity by keeping track of millions of variables as we develop formal computational models that quantitatively describe principles and hypotheses regarding brain function. Computers help us to explore models, and to crystallize predictions to be tested in the lab.

Measuring the myriad variables and complex interactions involved in brain function, and perturbing brain activity in a precisely controlled manner, require better tools than we have now. Quantitative sciences such as physics, chemistry, engineering, and computer sciences, are essential to developing the ground-breaking instrumentation that will let us work and see deeper and better into the brain.

Princeton is at the very forefront of work on all of these challenges, as well as other related challenges in the biological sciences. The QCN program will train researchers to address these challenges. They are some of the  key problems that we face as we try to solve one of the great mysteries of the world: how do our brains work?

Michael Graziano's lab has used topographical computational modeling to study how motor cortex develops

Michael Berry's lab has developed methods for recording from almost every retinal ganglion cell within a patch of retina.

Carlos Brody's lab has developed computational models of prefrontal cortex that combine short-term memory with decision-making