What is computational neuroscience?

Neuroscience, the scientific study of the nervous system, is an interdisciplinary field that incorporates biology, chemistry, computer science, engineering, mathematics, medicine, philosophy, physics, and psychology. Computational neuroscience employs theoretical tools to explain, predict, or interpret experimental data and the complex mechanisms that underlie it. Some examples of the use of quantitative approaches in neuroscience include:

Advanced data analysis
Developing tools to extract more understanding and information from neural data on multiple scales

Dynamical and statistical modeling
Using data to construct models that test the role of specific neural mechanisms

Theory

  • Developing novel conceptual models that explain or bring together disparate observations and make new predictions and suggest new analyses
  • Identifying minimal structure or dynamics that explain a key neural phenomenon
  • Identifying computational/algorithmic motifs and design principles that optimize and explain neural function

Training Opportunities

Since 2011, an NIH training grant supports 2-year graduate traineeships and stipends for undergraduate research for undergraduate scholars who have committed to fulfilling the requirements of the Computational Neuroscience program. The program is directed by Dr Adrienne Fairhall (Physiology & Biophysics) with the assistance of a Leadership Team, Drs David Perkel (Biology/Otolaryngology), William Moody (Biology), Fred Rieke (Physiology & Biophysics) and Eric Shea-Brown (Applied Math).