Computational Cognitive Neuroscience

Computational Cognitive Neuroscience -- Spring 2019

Instructor: Peggy Seriès
Lectures :on Mondays and Thursdays 11.10 am
Labs : time and place TBA
On wk1 students will be offered a non compulsory matlab tutorial covering basic matlab prerequisites.
TA & Tutor: Samuel Rupprechter and Frank Karvelis.

This is a 10 points course for MSc level students. There are no prerequisites, no prior knowledge in neuroscience is necessary but some background in statistics, calculus, linear algebra is required, as a well as some knowledge of programming. We will use matlab (or python).

Computational Cognitive Neuroscience is a growing research field. The aim of this course is to learn the tools and concepts that can be used to model the relation between the activity of the brain and cognitive processes. This course will appeal to students who are interested in the basic principles of computation in the human and animal brain, in particular how we can relate the activity of the brain to perception, behaviour or decision-making.

This course differs from / complements NC and NIP in focussing on 'higher level' processes and phenomena (e.g. decision making) and more conceptual models (even if we'll try to stay as close as possible to neurophysiology). It is a good complement to those courses if you are interested in a PhD in computational neuroscience.
The topics discussed in the course are mostly of interest for academic research and although they have inspired machine learning solutions, they are of little direct applicability. Tools from machine learning, though, are commonly used in this field.
Apart from learning about the brain, you will also learn about numeral modelling of differential equations, random processes, decoding techniques, dynamical systems, Bayesian modelling and more.

This year, the course material will be posted on Learn.
The information below relates to last year.

Resources

Brain Facts

introduction to matlab

Matlab primer

Assignments

Two assignments (25% each) and one report (50%) no exam.
The assignments will be posted on Learn.

Labs

The labs will consist mainly of implementation in matlab of simple models of population codes, perception, learning and plasticity and decision making. Attendance and work on this material will help for the assignments.

week 1: Optional Review of Pre-requisites: matlab tutorial (recommended if you don't have a strong background in programming).
will take place:
TBA**

references: basic tutorial -- more extensive one.
For the lab, we will use the following material:
Exercises -- Elementary Graphs.

week 3 -integrate and fire neuron. Lab2.pdf.
week 5 -the ring model. Re-implementating: Ben Yishai et al, 1995. Lab3.pdf
week 7 - Bayesian multisensory integration Lab4.pdf. Here is the paper by Wei Ji Ma et al (2006) .

Example answer code for each lab will be distributed after each lab.

Classes

The course slides will be on Learn.

Here is the material from last year for reference, the course this year will be slightly different.

18/09/17: Introduction to the course. lecture slides .

21/09/17: Encoding. Overview of the brain, neurons, synapses and electrophysiology. Responses in visual cortex. lecture slides

25/09/17: Encoding: Variability in the brain. Poisson and Gaussian models of spike count distributions. lecture slides

28/09/17: Applications of Encoding. Review of the visual system. introduction to Decoding. Lectures slides. Reading: Visual prostheses for the blind.

01/10/17: Decoding. Application to Brain-Machine interfaces. Basics of Estimation theory. Lectures slides Reading: Reading Minds; more advanced: BMI beyond neuro-prosthetics.

05/10/17: Fisher Information. Applications of the Encoding-Decoding model in research problems. Lectures slides

09/10/17: Models of neurons. Lectures slides.

12/10/17: Models of networks of neurons. Lecture slides.

16/10/17: Supervised learning in networks of neurons. Lecture slides .

19/10/17: Reinforcement learning in computational neuroscience. Lecture slides .

23/10/17: Sam Rupprechter teaching: Reinforcement learning models to understand Depression. Lecture slides.

26/10/17: Sam Rupprechter teaching: RL models to understand Depression (continued). Lecture Slides

30/10/17: Information about assignment 3 (slides). Models of Working Memory. Lecture Slides..

02/11/17. Working memory (continued). Decision Making. Lecture slides . reading: Decision-making as a window on cognition