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Unit information: Learning, Computation and the Brain in 2022/23

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

Unit name Learning, Computation and the Brain
Unit code COMSM0094
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Houghton
Open unit status Not open
Units you must take before you take this one (pre-requisite units)

A basic knowledge of programming in Python, Julia or MATLAB.

A basic knowledge of probability theory and of differential equations.

Units you must take alongside this one (co-requisite units)


Units you may not take alongside this one
School/department Department of Computer Science
Faculty Faculty of Engineering

Unit Information

This unit will introduce the neuronal dynamics supporting biological computations performed in the brain in single neurons and by network of neurons. It will describe our current understanding of how the brain learns and computes, starting with dynamical models of single neurons, working up to the dynamics of small networks of neurons, and plasticity and learning in those networks.

Examples will be covered from sensory, decision making, information processing, and long-term memory systems in the brain. It will explore current research into the relationship between neural computation and machine intelligence, covering how both deep learning and Bayesian models share similarities with brains, and how the brain has helped us in trying to develop machine and robotic intelligence.

Please note: You cannot take this unit if you have previously taken COMS30017 Computational Neuroscience (Teaching Unit) or associated units.

Your learning on this unit

On successful completion of the unit, students will be able to:

  1. mathematically describe and computationally implement models of single neurons and brain networks;
  2. describe and critically evaluate state-of-the-art ideas, methods and challenges in the fields of computational neuroscience and machine learning;
  3. identify and discuss the similarities and differences between biological and machine intelligence;
  4. read and understand current research literature in models of cognition

How you will learn

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

How you will be assessed

A single, 2 hour exam that will cover all the ILOs.


If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMSM0094).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.

See the Faculty workload statement relating to this unit for more information.

The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit. The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.