Cognitive & Systems Neuroscience Track

The goal of the Cognitive and systems neuroscience track is to train neuroscientists able to bridge the diverse range of approaches and techniques required to investigate the brain mechanisms for complex behavioral phenomena such as learning, memory, attention, language, cognition, emotion, sensation/perception, movement, the wakefulness-sleep cycle, response to pain, and feeding.

These phenomena depend on the integrated functioning of neural circuits and systems, and may be investigated using non-invasive imaging of brain structure and function (e.g. fMRI and MEG) and advanced methods for recording neural structure and function associated with specific cognitive and behavioral processes in vivo and in vitro. In particular, cognitive and systems neuroscience track demands expertise at many levels.

At the highest level, psychological approaches to studying mental phenomena form the historical foundation for the field. At an intermediate level, models of how psychological processes, such as memory, are instantiated in neural circuits, must be constructed in order to generated experimentally testable hypotheses.

At the lowest level, the latest advanced methods, such as multi-photon imaging and genetic techniques, must be learned and applied in order to make major advances. Researchers in the UT Neuroscience Program span the broad gamut of interests encompassed by Cognitive and systems neuroscience.

Requirements

In addition to completing the core requirements of the Neuroscience program, the Cognitive and Systems Neuroscience track student is required to complete the following courses:

  • Cognitive Neuroscience I
  • Systems Neuroscience

Elective courses in systems and cognitive neuroscience from the GSBS curriculum,, as well as from Baylor College of Medicine, Rice University and University of Houston may also be taken, in consultation with the students’ mentor. Each student’s complete course schedule will be determined in consultation with their individual mentors, and with the graduate advisor for the track, Michael Beauchamp.