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Unit information: Information Processing and the Brain (Teaching Unit) in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Information Processing and the Brain (Teaching Unit)
Unit code COMSM0075
Credit points 0
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Houghton
Open unit status Not open
Pre-requisites

COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent.

A knowledge of Python or Julia.

A basic knowledge of probability theory and of differential equations.

Co-requisites

EITHER Assessment Units COMSM0073 Information Processing and the Brain (Exam assessment, 10 credits)

OR COMSM0074 Information Processing and the Brain (Coursework assessment, 20 credits).

Please note:

COMSM0075 is the Teaching Unit for the Information Processing and the Brain option.

Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMSM0073) or coursework (20 credits, COMSM0074) by selecting the appropriate co-requisite assessment unit.

Any other students that are permitted to take the Information Processing and the Brain option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMSM0073).

School/department Department of Computer Science
Faculty Faculty of Engineering

Description

This unit explores information processing, statistical and deep learning in neuroscience. It starts out with an overview of information, statistical theory and the probabilistic brain before focusing on computational models of neural circuits and learning, including unsupervised, supervised and reinforcement learning, visual and auditory system, convolution and recurrent neural networks and the backpropagation algorithm in the brain. Finally the unit explains how to relate these models to neural data.

Overall, the unit will enable students to understand how concepts from data science, machine learning and computational modelling are being used to solve one of the most challenging problems in science: how do our brains work.

Intended learning outcomes

General ILOs

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

  1. Demonstrate an understanding of state-of-the-art computational models being used to understand brain functioning.
  2. Demonstrate an understanding of different forms of modelling in neuroscience, from probabilistic models to neural networks.
  3. Relate deep learning networks to the brain.
  4. Be familiar with different forms of learning in the brain (and machine learning).
  5. Perform advanced data analysis for real-world problems.
  6. Read current research literature in models of cognition.

When assessed by Examination, in addition to the general ILOs above, students will be able to:

  1. Demonstrate an understanding of computational models of brain functioning and of information theory.
  2. Use tools from information theory, probability and deep learning to interpret the behaviour of neuronal and neural networks.

When assessed by Coursework, in addition to the general ILOs above, students will be able to:

  1. Simulate and interpret cognitive and electrophysiological data using modern models of brain functioning.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures and self-directed exercises.

Teaching will take place over Weeks 1-7, with coursework support in Weeks 8-10 and for students assessed by examination, consolidation and revision sessions in Weeks 11 and 12.

Assessment Details

Examination details:

January timed assessment (100%, 10 credits)

OR

Coursework details:

Coursework (100%), to be completed in weeks 8-10.

Reading and References

A list of textbooks and of relevant research papers will be provided.

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