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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Neural Computation (INFR11162)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
Summary**This course replaces Neural Computation (INFR11008)**

This module aims to examine:

How the brain computes and processes information from the outside world.

How the brain wires up and how it stores information.

We will study the brain at a fairly low level, so that we can make contact with neurophysiological data. We will show the necessary biological data and how it can be described in mathematical terms. We will present modelling methods applicable to various levels of organisation of the nervous system (e.g. single cells, networks of cells). We discuss models of particular brain subsystems.

In the practical session we use Matlab and NEURON to simulate the models (No familiarity with NEURON required, some self study of Matlab is beneficial.)
Course description *Introduction and overview of the brain
*The neuron
*Biophysical and reduced models of neurons
*Computation and coding in the brain
*Networks of neurons
*Early and higher visual processing
*Network-level modelling
*Plasticity and learning

Relevant QAA Computing Curriculum Sections: Simulation and Modelling, Artificial intelligence
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations 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.

Experience in programming or simulation systems desirable. Fair amount of mathematics (first order differential equations, eigenvectors, descriptive statistics). No background in Neuroscience is necessary.
Information for Visiting Students
Pre-requisitesExperience in programming or simulation systems desirable. Fair amount of mathematics (first order differential equations, eigenvectors, descriptive statistics). No background in Neuroscience is necessary. Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course lecturer.
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 20, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 58 )
Additional Information (Learning and Teaching) There will be one assessed assignment, one assignment with formative feedback, and an exam.
Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 75%
Coursework 25%
Practical Exam 0%

There will be one assessed assignments, one assignment with formative feedback, and an exam.
One assignment - 25%
Exam - 75%

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.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a basic knowledge of neuroscience and neural computation.
  2. Abstract neuroscience experimental data to a model and should be able to critically evaluate these models.
  3. Understand the major limitations in verifying the model experimentally.
Reading List
* Supplementary reading list below (Detailed lecture notes are provided)
* Shepherd, G. M. (1994). Neurobiology. Oxford University Press, New York, third edition.
* Abbott and Dayan (2001) Theoretical Neuroscience . MIT press (recommended)
* Koch, C. and Segev, I., editors (1998). Methods in Neuronal Modelling: From Ions to Networks. MIT Press, Cambridge, Massachusetts, second edition.
* Churchland, P. S. and Sejnowski, T. J. (1992). The Computational Brain. MIT Press, Cambridge, Massachusetts.
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Matthias Hennig
Tel: (131 6)50 3080
Course secretaryMr Gregor Hall
Tel: (0131 6)50 5194
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