PSL1071H - Advanced Topics: Computational Neuroscience

Course Coordinator:  F. Skinner

Description:
Computational neuroscience seeks to understand how the brain and nervous system compute. This highly interdisciplinary field requires both experiment and theory and encompasses several disciplines including physiology and mathematics. This course will focus on selected computational neuroscience aspects such as types of neuron and network models, and techniques from dynamical systems theory that are used to analyze different models. The emphasis in this course will be on understanding the neurobiological basis and assumptions in models and insights and understanding that can be achieved from the models and analyses.

The overall objective of this course is to foster an appreciation for combinations of modeling, experiment and theory in the field so that students can read and critically evaluate computational neuroscience papers. This course is expected to enhance collaborative research training by teaching students how to interact as well as expanding and enriching their view of theoretical and non-theoretical research interactions in the future. This course is also meant to help break down communication barriers between different disciplines and to encourage dialogue between theoretical and non-theoretical type individuals.

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. Over 10 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 courses.

Evaluation:
Oral presentations: 40%
Part (i) 10%; Part (ii) 30%.

Written: 40%
Part (i) 10%; Part (ii) 30%.

Class participation: 20%
Questioning and participation during journal club paper presentations and participation in questioning and discussions in general.

Details will be provided in class.

Remarks:
This class requires a minimum enrolment of 4.

Disclaimer: 
Not a methods course (there are workshops for this and specific courses can cover nonlinear dynamics etc.) or an attempt to cover everything, although some basic background will be provided.  It is the integration, interdisciplinary aspect to be examined through journal articles (as mentioned in course outline) and one is encouraged to learn and study further on one’s own and/or through additional courses and workshops.

Last updated: 13-Nov-2015

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