Computational Cognitive Neuroscience

Computational Cognitive Neuroscience -- Spring 2016

Instructor: Peggy Seriès
Lectures : Monday/Thursday 11:10 - 12.00, Zone: Central. G.8 Gaddum LT 1GSQ. starting Monday January 11th 2016
Labs : Friday 1-4 pm, FH 1.B31, Weeks 2,4,6,8,10 (Students are only expected to attend for 2 hours in the 3 hour slot).
Tutor: Peggy Series.

This is a course for MSc level students. There are no prerequisites but some background in statistics, calculus, linear algebra will help, as a well as some knowledge of matlab.

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 cognitive processes.
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).

see last year's webpage.

Resources

No proper textbook. We will use Dayan and Abbott's Theoretical Neuroscience, MIT Press and O Reilly's Computational Explorations in Cognitive Neuroscience, MIT Press, and various journal articles / book chapters that I will provide.
The Bayesian Brain, a review by A. Pouget and D. Knill (2004).

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 (distributed Jan 25th) Deadline 12/02/2016 4 pm (please note change !!).
Matlab assignment 2 (distributed Feb 22nd) Deadline 14/03/2016 4 pm. Inspired from the paper by Smith et al 2007: Linking Animal Models of Psychosis to Computational Models of Dopamine Function.

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. 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 non-specialist 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: 01/03/2016. Please send me the pdf of your paper by email.
deadline for report: 01/04/2016 4 pm. 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

Information from last year, subject to changes : // 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.

-- Review of Pre-requisites: matlab tutorial -TBA
references: basic tutorial -- more extensive one.
For the lab, we will use the following material:
Exercises -- Elementary Graphs.

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.

Lab 2: integrate and fire neuron. Lab2.pdf.
lab 3: the ring model. Re-implementating: Ben Yishai et al, 1995. Lab3.pdf
lab 4: Bayesian multisensory integration Lab4.pdf. Here is the paper by Wei Ji Ma et al (2006) .

Classes

** Indicative only; Slides of last year -- Subject to changes **

Introduction. lecture slides.
Encoding. Overview of the brain, neurons, synapses and electrophysiology. Responses in visual cortex.
Readings: Chap 1 in D&A. lecture slides.
Encoding and decoding. Lecture slides [encoding slides] . [decoding slides (updated Jan 2014)]
decoding, Fisher information [lecture slides] -- updated Jan 2012, Modeling Neurons [lecture slides]
Modeling networks [lecture slides] -- Ring model and working memory (updated Feb 14) [lecture slides]
Readings: Ring model:Ben Yishai et al, 1995; Working Memory: Constandinis and Wang, 2004.
Bayesian Brain [slides]
Networks that learn [slides ]
Perceptual Learning
Reinforcement learning in the brain [slides]
Decision making [slides]
Neuroeconomics. [lecture slides]. readings: Loewenstein08, Glimcher08.
Models of Addiction and the emerging field of Computational Psychiatry [slides]

This page is maintained by Peggy Series.