Undergraduate Course: Computational Neuroscience (INFR11209)
Course Outline
School  School of Informatics 
College  College of Science and Engineering 
Credit level (Normal year taken)  SCQF Level 11 (Year 4 Undergraduate) 
Availability  Available to all students 
SCQF Credits  10 
ECTS Credits  5 
Summary  In 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 bottomup and topdown 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)
Prerequisites 

Corequisites  
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
Prerequisites  As 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:
 describe and critically analyse fundamental concepts and approaches to studying neuroscience and neural computation
 abstract neuroscience experimental data into an appropriate computational model and critically evaluate such a model from a biological and/or computational perspective
 given a neuroscientific problem, identify an appropriate modelling approach to that problem and compare the strengths and weaknesses of alternative modelling approaches.
 apply probabilistic, informationtheoretic, and machine learning techniques to model neural function and evaluate the neurobiological implications of such models
 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: problemsolving, critical/analytical thinking, handling ambiguity, knowledge integration
Communication: crossdisciplinary communication 
Keywords  CNS,Neuroscience,Cognition,Biology,Computational Model,Brain 
Contacts
Course organiser  Dr Angus Chadwick
Tel:
Email: angus.chadwick@ed.ac.uk 
Course secretary  Miss Yesica Marco Azorin
Tel: (0131 6)505113
Email: ymarcoa@ed.ac.uk 

