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Computational Neuroscience

Tom Palmeri
507 Wilson Hall
615-343-7900 (office)

We study how people visually categorize, identify, and recognize objects. We examine how objects are processed and represented by the visual system, how visual knowledge about objects is represented and learned, and how perceptual decisions are made. We are particularly interested in the temporal dynamics of visual object recognition, which includes the short-term dynamics of an individual decision about an object and the long-term dynamics of how those decisions change with learning and expertise. We are interested in how the temporal dynamics of behavior are related to the temporal dynamics of neural activity in the brain.

Computational neuroscience is one approach we use to understand these processes. Hypotheses about brain mechanisms are formalized in mathematical and computational models that are simulated on computers. Simulated predictions are compared to observed behavior and neural activity. Competing models about brain mechanisms are assessed by how well or how poorly they predict. Model predictions motivate new behavioral and neural experiments.

Some of our research has been highlighted by Research News@Vanderbilt:
New insight into impulse control
Neurons cast votes to guide decision-making
Racing neurons control whether we stop or go

Students interested in combining computation and mathematics with neuroscience and psychology might be interested in exploring computational neuroscience as an independent research topic. Students would ideally have had at least one semester of computer programming and one semester of calculus.

Students interested in exploring computational neuroscience might consider the new undergraduate minor in Scientific Computing at Vanderbilt, which was highlighted by Vanderbilt News. Students in the minor in Scientific Computing are taught techniques for understanding complex physical, biological, and social systems. Students are introduced to computational methods for simulating and analyzing models of complex systems, to scientific visualization and data mining techniques needed to detect structure in massively large multidimensional data sets, to high performance computing techniques for simulating models on computing clusters with hundreds or thousands of parallel, independent processors and for analyzing terabytes or more of data that may be distributed across a massive cloud or grid storage environment.

For more information, please visit the lab website.