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 how computations carried out by the nervous system leads to cognition, in particular perception, memory, learning, and decision-making. We learn to develop and simulate computational models that incorporate data from neurobiology and / or can be used to model aspects of cognition such as measured during behavioural experiments.
Such models can be used to understand individual differences and mental disorders (e.g., autism, schizophrenia, addiction, and depression): a domain of application that is emphasised in the second half of the course is the emerging field of computational psychiatry.
- Overview of computational neuroscience basics (models of neurons and networks)
- Reinforcement learning models for computational neuroscience
- Bayesian models for computational neuroscience (The Bayesian Brain)
- Computational modelling of behavioural data
- Models of decision-making
- Application to individual differences (e.g., autism) and mental disorders (e.g.,schizophrenia, addiction, and depression): introduction to Computational Psychiatry
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Computational Neuroscience (INFR11209)
|Prohibited Combinations|| Students MUST NOT also be taking
Computational Cognitive Neuroscience (UG) (INFR11233)
||Other requirements|| MSc students must register for this course, while Undergraduate students must register for INFR11233 instead.
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.
No prior biology / neuroscience knowledge is required. The course was developed assuming a background in computer science or related quantitative field. We use a small subset of not very advanced math and machine learning in the lectures.
Basics of Python or MATLAB is required.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, 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)
|No Exam Information
On completion of this course, the student will be able to:
- describe current computational theories of the brain and mental illness
- read, understand, and have a critical opinion on scientific articles related to computational cognitive neuroscience and computational psychiatry
- write and analyse simple computational models related to brain function in Python or MATLAB
- write scientific reports on topics related to computational cognitive neuroscience
|Graduate Attributes and Skills
|Additional Class Delivery Information
||Students should expect to spend approximately 40 hours on the coursework for this course.
|Keywords||linear differential equations,Bayesian inference models,model fitting,model comparison
|Course organiser||Dr Peggy Series
Tel: (0131 6)50 3088
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701