Postgraduate Course: Computational Cognitive Neuroscience (INFR11036)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||In this course we study computational approaches to understanding cognitive processes, using massively parallel networks. We study biologically-inspired learning rules for connectionist networks, and their application in connectionist models of perception, memory and language.
*Encoding Information in populations of neurons.
*Decoding Information from populations of neurons.
*Models of Neurons and Networks of Neurons.
*Information transmission and Attention.
*Models of Learning and Plasticity.
*Models of Memory.
*Models of Decision Making.
*Models of Mental disorders.
*The Bayesian Brain.
Relevant QAA Computing Curriculum Sections: Artificial Intelligence
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Some background in statistics, and calculus. Background in linear algebra and programming in Matlab is desirable.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 15,
Supervised Practical/Workshop/Studio Hours 15,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||The course is assessed by four assignments and a report.
You should expect to spend approximately 40 hours on the coursework for this course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
|No Exam Information
| 1 - Describe a cognitive architecture of the brain.
2 - Contrast the applicability of several connectionist learning rules.
3 - Understand the limitation of current connectionist models.
4 - Design a simple computational model of a cognitive process and relate it to the literature and understand the underlyng assumptions.
5 - Write a simple memory model in PDP++
|Computational Modelling in Cognition: Principles and Practice by Stephen Lewandowsky and Simon Farrell, Sage 2011|
|Course organiser||Dr Peggy Series
Tel: (0131 6)50 3088
|Course secretary||Ms Katey Lee
Tel: (0131 6)50 2701
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:26 am