CCN: Computational Cognitive Neuroscience

Welcome to Computational Cognitive Neuroscience

Course Contacts

The lecturer for this course will be Peggy Seriès.

TA: Lars Werne

Course Content

The title of this course could really be "Computational Cognitive Neuroscience and Computational Psychiatry".

In this course we study how computations carried out by the nervous system lead to cognition, in particular perception, learning, and decision-making. we incorporate data from neurobiology and behavioral experiments, simulate certain aspects of it, and try to formulate theories about the brain. A domain of application that is strongly emphasized is the field of mental illness. This course can be seen as an introduction to computational psychiatry.

Apart from learning about the brain, healthy cognition and mental illness, you will also learn about numerical modelling of differential equations, probabilistic models, reinforcement learning models applied to human learning, as well as current research and pitfalls in modelling cognition and mental disorders.

For whom is this course?
This course should appeal to students who are interested in models of computation in human and animal brains, as well as mental illness. It complements other courses in the cognitive sciences, offering a more biological perspective.

For whom is it not?
The topics discussed in the course have inspired many machine learning solutions to real-life problems, however, we shall hardly discuss those. It should also be noted that the course has limited direct practical applicability outside academic research.

Prerequisites

No prior biology/neuroscience/psychiatry knowledge is required. The course was built assuming some background in computer science or related quantitative field, in particular familiarity with coding. We use a small subset of not very advanced math and machine learning in the lectures. Keywords: linear differential equations, Bayesian inference models, model fitting and model comparison. Please make sure you have experience with these concepts before enrolling in the course.

The course "Computational Neuroscience" (CNS) is highly recommended.

In the tutorials we use Python (or Matlab if you prefer but the tutors will be using Python).
Basics of Python is thus required is required. Prior experience with programming is required.

Learning Outcomes

On successful completion of this course, you should be able to:

1. describe current computational theories of the brain and mental illness

2. read, understand, and have a critical opinion on scientific articles related to computational cognitive neuroscience and computational psychiatry

3. write and analyse simple computational models related to brain function in Python or MATLAB

4. write scientific reports on topics related to computational cognitive neuroscience

Course Outline

- Overview of computational neuroscience basics (models of neurons and networks)
- Reinforcement learning models for computational neuroscience
- Bayesian models for computational neuroscience (The Bayesian Brain)
- Computational modelling of behavioural data
- Models of decision-making
- Application to individual differences (e.g., autism) and mental disorders (e.g.,schizophrenia, addiction, and depression): introduction to Computational Psychiatry