Cogmaster, Course CA6a
(course homepage here)

This page will be regularly updated.

Rem.: some documents can be accessed only with the login and password given to registered students;
these documents are provided as course support, and cannot be distributed or used for anything else without authorization.


This page:

General and useful references: see bottom of this page.

Specific material for the Course, current Academic year: see below

Introduction and basic tools, models and concepts

Class 1, Oct 5:
(Biophysics of) Neurons I: Basic electrical properties and simplest model
slides (pdf)

Class 12, Oct 6: (Biophysics of) Neurons II.
presentation: slides (pdf)
lecture notes (taken by students, academic year 2015-2016).
Hodgkin & Huxley: access to papers here.

Class 3, Oct 19: Synapses.
presentation: slides (pdf)
lecture notes (taken by students, academic year 2015-2016).
related papers here.

Class 4, Oct 26: Balanced networks.
lecture notes (taken by students, academic year 2015-2016).
presentation: slides (pdf)

Class 5, Nov. 11: Rate models
Rate models: slides (pdf)
Papers: dynamics
(Wilson and Cowan 1972; Seung 2003; Murphy and Miller 2009)
Papers: binocular rivalry
Papers: V1

Class 6, Nov. 16: Learning I.
Different kinds of learning: supervised, unsupervised and reward learning.
Supervised learning: learning associations
Hebbian learning with binary synapses: the Willshaw model.
Course material
Supervised learning by the Perceptron: capacity, information storage.
Perceptron: Papers
Hertz, Krogh, and Palmer (1991) (see general references below), Chap. 5.
Unsupervised learning
The Oja model
presentation: slides (pdf)
E. Oja, A simplified neuron model as a principal components analyzer, J. Math. Biol. 15 (1982) 267-273.
Hertz, Krogh, and Palmer (1991) (see general references below), Chap. 8.2 pp. 199-210

Class 7, Nov. 23: Learning II. Behavioral learning
slides (pdf)
lecture notes (taken by students, academic year 2015-2016).
Papers here. See in particular:
- Kaelbling et al, 1996
(an exhaustive survey on Reinforcement Learning and Dynamic Programming methods for Markov Decision Processes)
- Sutton, 1988
("the" TD learning paper)
- Grandman et al, 2012
(a somewhat technical survey on actor-critic methods)
- Bromberg-martin et al, 2010
(a recent survey of dopamin signaling and its role in learning)
- Schultz, Dayan and Mantague, 1997
(the pioneering study showing that DA neuron resemble a reward prediction error)
- Gallistel, 2003
(a stimulating paper on classical conditioning)

Class 8, Nov. 30: Coding.
Presentation: slides (pdf)
More course material on neural coding here.

Models of specific cognitive systems

Class 9, Dec. 7: Vision

Presentation: slides (pdf)

Class 8, Dec. 14: Navigation

Presentation: slides (pdf)

Class 11, Dec. 21: Memory

Presentation: slides (pdf)

Class 12, Jan 11: Perceptual Decision Making

Presentation: slides (pdf)

More course material will be posted here.

Problem sets are/will be posted here

Exam: instructions are/will be posted here

Archives, specific material for this Course:

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Most, if not all, the following books are available at the RISC library
and at the library of Institut Henri Poincaré.


* Computational neuroscience books


Abbott and Dayan, "Theoretical Neuroscience" (MIT Press, 2001)

A nice overview of the field of computational neuroscience, at a level comparable to that of the course.


Gerstner, Kistler, Naud and Paninski, "Neuronal Dynamics: From single neurons to networks and models of cognition" (Cambridge University Press, July 2014)

Narrower scope, but a nice online version.


Ermentrout and Terman, "Mathematical Foundations of Neuroscience" (Springer, 2010)

A book with detailed mathematical treatments, with a focus on the dynamics of neural activity.


Tuckwell, "Introduction to Theoretical Neurobiology", Vols. I & II (Cambridge U. Press, 1988)

Again mathematical treatment, with narrower scope and emphasis on single-neuron dynamics and statistics of neural activity.


Koch, "Biophysics of Computation" (Oxford U. Press, 1999)

A detailed presentation of computational aspects of single-neuron behavior, closer to biology than Tuckwell's book (but more restricted in topics also).


Hertz, Krogh, and Palmer, "Introduction to the Theory of Neural Computation" (Addison-Wesley, 1991 - now from: Perseus Book Group and Westview Press)

A good reference book on memory models in the perspective of statistical physics (and on many other neural network models). This book is more advanced mathematically than the three other and a good resource for learning useful statistical physics techniques.


Rieke, Warland, de Ruyter van Steveninck, and Bialek, "Spikes" (MIT Press, 1997)

A nice introduction to the statistical and information theoretic approach to neural coding, with a title length inversely proportional to the number of authors.


Izhikevich, "Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting" (MIT Press, 2007)


* General neurobiology books:


Nicholls, Martin, Wallace, and Fuchs, "From Neurons to Brain" (Sinauer Associates, 2001, 4th ed.)

A readable and well-balanced introductory survey of neurobiology.


Kandel, Schwartz, and Jessel, "Principles of Neural Science" (McGraw-Hill, 4th edition, 2000)

Another successful neurobiology survey text.


Hubel, "Eye, Brain, and Vision" (Scientific American Library, 1988 and 1995)

A delightful introduction to visual processing by the brain, at an undergraduate/interested general reader level.


Johnston and Wu, "Foundations of Cellular Neurophysiology" (MIT Press, 1994)

A comprehensive introduction to the biophysics of neurons, with a quantitative leaning.


Fain, "Molecular and Cellular Physiology of Neurons" (Harvard University Press, 1999)

A nice very read on the physiology of single neurons and synapses, at an introductory level, with a lovely final part on sensory transduction.



* Periodicals:


A number of journals are devoted to computational neuroscience; these include Neural Computation, The Journal of Computational Neuroscience, Network: Computation in Neural Systems, Biological Cybernetics, Neural Networks. Other general or topical science journals also publish computational neuroscience articles; prominently, Nature, Nature Neuroscience, Science, Neuron, PNAS, PloS (free, online), The Journal of Physiology, The Journal of Neuroscience, The Journal of Neurophysiology, Cerebral Cortex, Vision Research, Trends in Neurosciences, Current Opinion in Neurobiology.

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