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Unit information: Computational Neuroscience (Teaching Unit) in 2020/21

Unit name Computational Neuroscience (Teaching Unit)
Unit code COMS30017
Credit points 0
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. O'Donnell
Open unit status Not open
Pre-requisites

Basic knowledge of Python or Julia programming languages would help as the courseworks (both formative and summative) will be best implemented in these languages. No previous neuroscience knowledge is required.

Co-requisites

EITHER Undergraduate Assessment Unit COMS30016 Computational Neuroscience (Examination assessment, 10 credits)

OR COMS30015 Computational Neuroscience (Coursework assessment, 20 credits)

OR Assessment Unit for M level Masters students, COMSM0039 Computational Neuroscience (10 credits)

Please note:

COMS30017 is the Teaching Unit for the Computational Neuroscience option.

Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMS30016) or coursework (20 credits, COMS30015) by selecting the appropriate co-requisite assessment unit.

M level Masters students must register for the M level assessment unit COMSM0039.

Any other students that are permitted to take the Computational Neuroscience option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30016).

School/department Department of Computer Science
Faculty Faculty of Engineering

Description

This unit Aims to provide the student with an understanding of computational principles of biological computations performed in the brain by single neurons and network of neurons, for the following brain processes:

  • learning & memory
  • visual processing
  • general sensory coding
  • and temporal dynamics of neurons.

Intended learning outcomes

General ILOs:

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

  1. Employ computational principles of the brain in their future engineering work.
  2. Undertake research on the brain with understanding of brain’s purpose (i.e., information processing).
  3. For each levels of abstraction (single neuron, network of neurons, interacting brain areas): understand the assumptions made by the models, validity of the assumptions, and computational principles.

In addition to the General ILOs above, when assessed by Examination, students will be able to:

  1. Have a good general understanding about the brain’s computational functions.
  2. Be able to derive and solve some of the key mathematical equations underlying classic computational models of brain function.

In addition to the General ILOs above, when assessed by Coursework, students will be able to:

  1. Be able to simulate simple models of neurons, networks, and cortical areas in Python or Julia.
  2. Be able to analyse a real experimental dataset of brain activity.

In addition to the General ILOs above, on successful completion of the unit, M level students will be able to:

  1. Simulate simple models of neurons, networks, and cortical areas in Matlab or Python.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities 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

Undergraduate Examination details:

January timed assessment (100%, 10 credits)

OR

Undergraduate Coursework details:

Coursework, to be completed over weeks 8-10. (100%, 20 credits)

OR

M level assessment details:

Coursework (100%)

Reading and References

Lecture notes will be provided. Background reading to include:

  • Dayan, Peter and Abbott, Larry F, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, 2001) ISBN: 978-0262041997

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