Modelling and simulation of biological neurons and neural circuits. Simulation as a problem-solving method. Using graphics, interactive simulations and digital media in scientific communication.
An introduction to computer modelling of neurons and nervous systems. Students will learn how to design and code different kinds of neuronal models, and how to use computer modelling to analyse neurons as information-processing and decision-making devices. We focus on mechanisms in real nervous systems, as distinct from the “neurons” of neural network theory and machine learning.
|Paper title||Special Topic: Computational Neural Modelling|
|Teaching period||First Semester|
|Domestic Tuition Fees (NZD)||$827.17|
|International Tuition Fees (NZD)||$3,157.40|
- 54 300 level points from Science Schedule C. 18 points from 100-level MATH, COMO or COSC.
The course is intended for science majors, especially NEUR majors, with possibly limited programming experience. The formal requirement is to have passed at least one 100-level paper in computing or modelling. Additional background in mathematics, physics or coding will be advantageous.
Associate Professor Mike Paulin email@example.com
- Teaching staff
- Paper Structure
The paper is taught as a series of modules, covering biophysical models, dynamical systems models, information theory and statistical models. We will look at the roles of evolution, development and learning in the construction of nervous systems in animals including humans.
- Teaching Arrangements
We will use the scientific programming language Julia, in the Atom IDE as well as in Jupyter computational notebooks, which are becoming a standard platform for Data Science.
Textbooks are not required for this paper. Readinga will be supplied.
- Graduate Attributes Emphasised
Critical thinking, Information literacy, Scholarship, Research
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete the paper will:
- Understand current problems in neuroscience and the role of modelling and simulation in solving them.
- Be able to write computer code to simulate biophysical, dynamical and statistical models of neurons and neural systems.
- Be able to interpret and clearly explain results of neurobiological computer simulations.