Computational Cognitive Neuroscience  Autumn 2017
Instructor:
Peggy Seriès
Lectures :***CHANGE OF ROOM*** : 5.3 Lister weeks 37 and 911 and Lecture theatre 1  7 Bristo Square for week 8. on Mondays and Thursdays in Teviot Lecture Room Medical School doorway 5
Labs : 15.1017.00 on Tuesdays (5.05 West Lab AT) and Wednesdays (4.12 AT), on wk 2, 4, 6, 8, 10. 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 will help, as a
well as some knowledge of 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 decisionmaking.
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.

Resources
 No proper textbook. We will use Dayan and Abbott's Theoretical Neuroscience, MIT Press and and various journal articles / book chapters that I will provide.

If you want to prepare for the course, 2 things would be beneficial:
i) read a primer about neuroscience, e.g. Brain Facts . ii) make sure you can program in Matlab (see below for tutorials). You can use for e.g. : Chris' handout introduction to matlab; or Matlab primer.

Assignments
 Two assignments (50%) and one report (50%) (no exam).
 Matlab assignment 1. Deadline: October 27th 4 pm.
Related to the Encoding and Decoding lectures.
The report should look like a scientific report, with description and discussion of the results (not a presentation of the code). The quality of the presentation will be taken into account in the final mark.
 Matlab assignment 2 distributed: Oct 27th Deadline: November 17th.
Related to Reinforcement learning lectures.
 Report: You will have to write a paper based on one or two
papers of your choice. The paper(s) should be related to the themes of
cognitive neuroscience and have a significant computational component (describe a mathematical model or use simulations).
It should describe an original piece of research (not be a review !).
Please contact me if you're not sure of your choice or want
suggestions. You should explore the context, critically evaluate it,
and discuss questions raised by these papers and maybe suggest further
experimental or theoretical work.
Your report should be around 3000 words (4000 words MAX)
all included (references, captions etc..). You should write for the
interested, but nonspecialist reader. You can look at the journal
Trends in Neuroscience (TINS) for how to construct such papers. The aim
of the paper is that you should demonstrate that you can read a paper
in computational cognitive neuroscience, understand its methods,
evaluate its claims and place it in perspective.
Deadline for submitting choice of paper: 8th November 2017. Please send me the pdf of your paper by email.
Deadline for report: 8th December 2017. please submit both pdf via submit and paper copy to ITO.
Tips for choosing a paper.
Examples of 2 good papers from previous years: paper1 (attention), paper2 (hallucinations in migraines).

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 Prerequisites: matlab tutorial (recommended if you don't have a strong background in programming).
will take place:
**Wednesday 20th September 2017, at 15.10 in 4.12 AT.**
references:
basic tutorial 
more extensive one.
For the lab, we will use the following material:
Exercises 
Elementary Graphs.
 week 2  Lab 1. From single cells to psychophysics. Modeling the
experiments of Newsome, Britten et al (1989).
ROC Analysis and
neurometric curve. Lab1.pdf.
For reference, original papers: Newsome et al, 1989,Britten et al, 1992 (you don't need to read those to do the lab). You will need to use a Poisson random number generator in matlab.
 week 4  Lab 2: integrate and fire neuron. Lab2.pdf.
 week 6  lab 3: the ring model.
Reimplementating: Ben Yishai et al, 1995. Lab3.pdf
 week 8  lab 4: 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 posted here before the class.
 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 BrainMachine interfaces. Basics of Estimation theory. Lectures slides Reading: Reading Minds; more advanced: BMI beyond neuroprosthetics.
 05/10/17: Fisher Information. Applications of the EncodingDecoding 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.
 26/10/17: Sam Rupprechter teaching: title TBA
 30/10/17: Models of Working Memory.
 Decision Making.
 Bayesian Brain Theories and applications.
 Computational Psychiatry, selected topics
In the meantime, slides from previous year can give a good idea of the content of the future classes:
