• Simple Models of Neurons and Networks
• Population codes and the relationship between Physiology and Behavior
• Learning and Plasticity models
• Perception and Attention models
• Decision Making models
• Models of mental disorders
• the Connectionnist approach to cognition : examples in Language
• the emerging Bayesian approach to cognition
• .. and more

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.
• There is now an active conference in computational cognitive neuroscience. Check it out.
• Attention: Braun's book, MIT Press
• Perceptual learning, MIT Press.
• Decision Making, review by Gold and Shadlen (2007)
• They call it Neuro-economics.
• Neural correlates of consciousness: Koch's pages.
• The Bayesian Brain, a review by A. Pouget and D. Knill (2004).
• Chris' handout introduction to matlab

Assignments

• Two assignments (50%) and one report (50%) (no exam).
  


• Matlab Assignment 1. Deadline : February 15th, 3 pm.


• Matlab Assignment 2 (subject to minor changes). Deadline March 24th, noon.


• 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.
  The deadline date is April 7th, noon.
  Tips for choosing a paper.

Labs

• Lab 1 (Jan 21st). 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).
  If needed, a Poisson random number generator in matlab.
  A possible solution.


• Lab 1-2. Same material + Matlab primer


• Lab 2 (Feb 4th).Tools of computational neuroscience.
  Neurons: integrate-and-fire and the model of Izhikevich. Lab2.pdf
  Which model to use for cortical spiking Neurons? , Izhikevich, 2004.
  Also useful, Simple model of spiking neurons, Izhikevich, 2003 (to download code for network).
.
• Lab 2-2 (Feb 8th). Same material.


• Lab 3 (Feb 18th). The ring model. Re-implementing: Ben Yishai et al, 1995. Lab3.pdf
• Lab 4 (March 7th). Perceptron and perceptual learning.

Week 1 (Jan 7th)

• Introduction. lecture slides.
• Encoding. Overview of the brain, neurons, synapses and electrophysiology. Responses in visual cortex.
  Readings: Chap 1 in D&A. lecture slides.

Week 2 (Jan 14th)

• Encoding (continued). Describing tuning curves and variability. Poisson and Gaussian models of noise. lecture slides.
  Further reading: Stein et al, 'Neuronal variability: noise or part of the signal', Nat rev Neuro, 2005.
• Decoding. Readings: Chap 3 in D&A. lecture slides.
  Further reading: Lebedev & Nicolelis, 'Brain-machine interfaces: past, present and future', 2004

Week 3 (Jan 21st)

• Decoding (end). From population codes to psychophysical performance. Fisher information. lecture slides (updated)
  
• When neural responses and perception change (1): Attention. Lecture Slides.
  Reading: Maunsell and Cook, 'The role of attention in visual processing', 2002.
  Further Reading: Itti & Koch, Computational modeling of visual attention, 2001.

Week 4 (Jan 28th)

• When neural responses and perception change (2): Visual Adaptation. Lecture Slides. Presentation of Assignment 1.
• no lecture. Time to begin working on the assignment.

Week 5 (Feb 4th)

• Modeling Tools and Examples. Modeling neurons. The integrate-and-fire model Lecture Slides.
  Readings: D&A sections Chap 5 (sections 5.4,5.5,5.8,5.9).
• Modeling networks of neurons. Firing rate models. Lecture Slides.
  Readings: D&A Chapter 7.

Week 6 (Feb 11th)

• The 'Ring' Model, attractors,Working Memory /Sustained Activity. Lecture Slides.
  Readings: Ring model:Ben Yishai et al, 1995; Working Memory: Constandinis and Wang, 2004.
  
• Working memory, associative memory, sustained activity (spiking) models. Lecture Slides.
  .

Week 7 (Feb 18th)

• no lecture
• Perceptual Learning, Specificity and Neural Locus Lecture Slides.
  Reading: Tsodyks and Gilbert, 'neural networks and perceptual learning 2004; Seitz and Watanabe, 'a unified model for perceptual learning', 2005.
  

Week 8 (Feb 25th)

• Perceptual Learning: how do we know what to learn? Lecture Slides.
• No lecture (Cosyne). Time to begin thinking about the paper assignment.

Week 9 (March 3rd)

• Guest Lecture: Liana Romaniuk, on models of schizophrenia. Lecture Slides
• correction of assignment 1.

Week 10 (March 10th)

• Neural networks that learn. Perceptrons, delta rule, multi-layer networks, backprop.
  Readings: D&A Chapter 8. Lecture Slides
• Decision making: data and models. Lecture Slides
  Readings: Gold & Shadlen, 2007.

Week 11 (March 17th)

• The 'Bayesian Brain'. Lecture Slides
  Readings: Knill & Pouget, 2004.

News

• Jan 24: You may be interested in some of the talks in the DTC Neuroinformatics course which I am organizing.The DTC students in the course attend these talks already, but other CCN students are very welcome.


• Jan 24: You are encouraged to apply to the DTC for PhD study. Of course, MSc students taking CCN will often have taken some courses that are part of the DTC MSc year, but we can be flexible in such cases.
  Also, I am happy to take PhD students next year, either with DTC funding or by applying to other grant sources (contact me).