## Welcome to Computational Neuroscience

### Course Contacts

The lecturer for this course is Angus Chadwick.

We have two TAs (Arthur Pellegrino and Isabel Cornacchia) who will run the computer labs and mark the assignment.

### Course Content

This is a course about computation in the nervous system - see the course drps page for details. Please bear in mind that the content covered in this course is *not* of practical use for e.g., machine learning or data science (although the transferrable skills you will acquire are useful). This course is best suited to those who want to learn about the brain and how we model it mathematically and computationally.

### Prerequisites

This course requires a strong background in calculus, linear algebra, and statistics. For example, you should be comfortable thinking about and applying matrix eigenvalues/eigenvectors, determinants, and inverses, Fourier transforms, expectations of random variables, Taylor expansions, etc. We use mathematics extensively throughout the course, and you will be required to perform derivations as part of the assignment and exam.

You should also be comfortable programming in Python (although you may use another language if you prefer). This will be required for the computer labs and the assignment (but not the exam).

There are no formal prerequisites in biology, but you will need to learn a lot of it during the course, and this will require that you do some independent reading around the lectures.

The biophysical models we cover involve some basic physics concepts (roughly at high school level). Again, there are no prerequisitives for this, but you are expected to learn these concepts if they are unfamiliar to you.

### Learning Outcomes

On successful completion of this course, you should 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

Course Outline

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.

License

All rights reserved The University of Edinburgh