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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Computational Neuroscience (INFR11209)

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
SummaryIn this course we study computation in neural systems. We will consider problems such as:

How do neurons work and how do they communicate with one another?
How do groups of neurons work together to form representations of the external world?
How are memories stored and retrieved in the brain?

We will employ a combination of bottom-up and top-down approaches, meaning that we study these problems both by modelling and simulating the biological hardware, and by taking inspiration from artificial intelligence to try to build theories of the brain.
Course description This course focuses on computation in the nervous system. You will be introduced to basic neuroscience concepts, learn about how computational models are used to simulate processes in the brain, and learn about theories for how the brain processes information and performs computations.

Course Content:

1. Introduction to basic neuroscience concepts
2. Models of neurons
3. Neural encoding
4. Neural decoding
5. Information theory
6. Network Models
7. Plasticity/learning

The course will be delivered through lectures and computer labs.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements No prior biology / neuroscience knowledge is required. This course requires knowledge of linear algebra, calculus, probability and statistics. In particular, we assume familiarity with vectors and matrices (including matrix inverse and eigenvectors), special functions (logarithm, exponential), integration and differentiation of basic functions, the Taylor expansion, probability distributions (Poisson distribution, univariate and multivariate normal distribution, exponential distribution), expectation and variance of random variables, and Bayesian inference (prior and likelihood, joint and conditional distributions, Bayes rule). We will make use of simple linear differential equations, but prior experience of these is not a prerequisite. Some basic physics concepts will be used (e.g., voltage, capacitance, resistance) but prior knowledge is not required. Basic programming skills (e.g. in Python+NumPy or in Matlab) are required for the tutorials and assessments.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  0
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 16, Supervised Practical/Workshop/Studio Hours 5, Feedback/Feedforward Hours 3, Revision Session Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 72 )
Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment) Coursework will involve implementing and/or analysing/discussing in more detail material from lectures.
Feedback Oral feedback will be provided in tutorial/lab sessions. Written feedback will be provided on the assignment, and an additional oral feedback session will be scheduled if there is sufficient demand.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Computational Neuroscience (INFR11209)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. describe and critically analyse fundamental concepts and approaches to studying neuroscience and neural computation
  2. abstract neuroscience experimental data into an appropriate computational model and critically evaluate such a model from a biological and/or computational perspective
  3. given a neuroscientific problem, identify an appropriate modelling approach to that problem and compare the strengths and weaknesses of alternative modelling approaches.
  4. apply probabilistic, information-theoretic, and machine learning techniques to model neural function and evaluate the neurobiological implications of such models
  5. implement the models and methods learned in lectures and critically evaluate the results in the context of neural computation
Reading List
Theoretical Neuroscience (Dayan and Abbott)
Neuronal Dynamics (Gerstner)
Additional Information
Graduate Attributes and Skills Research and enquiry: problem-solving, critical/analytical thinking, handling ambiguity, knowledge integration

Communication: cross-disciplinary communication
KeywordsCNS,Neuroscience,Cognition,Biology,Computational Model,Brain
Course organiserDr Angus Chadwick
Course secretaryMiss Yesica Marco Azorin
Tel: (0131 6)505113
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