Computational neuroscience seeks to understand the fundamental principles of neural dynamics, and how the brain and nervous systems compute. This highly interdisciplinary field requires both experiment and theory, and encompasses several disciplines that include physiology, mathematics and engineering. This course will provide a brief background of neurobiological and mathematical concepts, describe the theory and ideas that underlie the models, analyses and methods used in the field today (e.g., Hodgkin-Huxley models, neural coding, oscillations). In addition, the course will provide an overview of the most recent approaches and applications of computational neuroscience in the fields of biomedicine.
Format:
This graduate-only seminar style course will satisfy part of the course requirement for the graduate program in the Department of Physiology. The course will expose graduate students to the range of research taking place in the Computational Neuroscience field and will create awareness of available resources.
The field of Computational Neuroscience is represented by annual meetings, journals specific to the field and is recognized via symposia, socials and topics at the annual Society for Neuroscience meeting. Physiological journals strongly encourage modeling and quantification. Almost 20 years ago, the chief editor of the Journal of Neurophysiology claimed that, "...Many of the most important findings in neurophysiology come from the use of quantitative methods of data analysis and from models of nervous system structure and function. Therefore we invite computational and theoretical papers that are strongly tied to the physiological analysis of the brain and nervous system." (J. Neurophysiol. 88:1, 2002).
The Department of Physiology has a history of theoretical physiology and encourages students with backgrounds in physical sciences. This course is a natural addition to the graduate program given the developments in the Neuroscience field today.
Prerequisite:
At least first year calculus and an introductory biology/neuroscience type course.
Evaluation:
Oral presentations: 40%
Written: 40%
Class Participation: 20%
