Postgraduate Course: Neural Computation (INFR11008)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	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 Synapses 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-requisites	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 2017/18, 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 )
Assessment (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment)	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 S2 (April/May)			2:00

Academic year 2017/18, Part-year visiting students only (VV1)		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 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info)	Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment)	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: Demonstrate a basic knowledge of neuroscience and neural computation. Abstract neuroscience experimental data to a model and should be able to critically evaluate these models. 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.