THEORETICAL NEUROSCIENCE

COURSE MATERIAL


(Cogmaster, Course CA6a)
Course homepage: 
French/ English version.

This page will be regularly updated - last update: January 16, 2012.

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General and useful references: see below.

Specific material for the Course, Academic year 2011/2012
(slides, papers, and problem sets).

Exam: see here



Class 1, Sept. 29: Introduction.


Class 2, Oct 6: (Biophysics of) Neurons.

presentation: slides (pdf) - slides (ppt)

Hodgkin & Huxley: access to papers here.


Class 3, Oct 13: Synapses.

presentation: slides (pdf)

related papers (here):
Short Term Plasticity
(Experimental)
Fatt-Katz-JPhysiol1952.pdf
DelCastillo-Katz-JPhysiol1954-a.pdf
Zucker and Regehr (2002) Annu. Rev. Physiol. (review paper)
Neher and Sakaba (2008) Neuron (review paper)
(Modeling)
Bertram et al. (1996) J. Neurophysiol.
Tsodyks-Markram-PNAS1997.pdf
Markram-Wang-Tsodyks-PNAS1998.pdf
Abbott-etal-Nelson-Science1997.pdf
Abbott-Regehr-Nature2004.pdf
Long Term Plasticity
(Experimental)
Bi and Poo (2001) Annu. Rev. Neurosci.
Sjostrom et al. (2001) Neuron
Malenka and Bear (2004) Neuron (review paper)
(Modeling)
Fusi et al. (2000) Neural Comp.
Shouval et al. (2002) PNAS
Graupner and Brunel (2007) PLoS Comput. Biol.

Class 4, Oct 20: Learning I.

Introduction
Different kinds of learning: supervised, unsupervised and reward learning.
Learning associations
Hebbian learning with binary synapses: the Willshaw model.
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
Reinforcement learning (see last Class)

Class 5, Oct 27: Learning II: recurrent networks of binary neurons.

presentation: slides
Course material:
Hopfield 1982
Derrida et al 1987
Books (see general ref. below): Amit, 1989; Hertz, Krogh and Palmer, 1991

Class 6, Nov. 10: Coding.

Presentation: slides (pdf)
General references on Information Theory:
T. Cover and J. Thomas, Elements of Information Theory, Wiley & Sons, New York, 1991. Second edition, 2006.
Richard E. Blahut, Principles and Practice of Information Theory, Addison-Wesley 1988.
On mutual information in somatosensory discrimination: Werner and Mountcastle 1965.
Optimal information processing (infomax)
On linear Gaussian models: slides (pdf)
Linsker, R. (1988), "An application of the principle of maximum information preservation to linear systems", NIPS 1988 (http://books.nips.cc/nips01.html)
R. Linsker, From Basic Network Principles to Neural Architecture (series), Proc. Nat'l Academy of Sciences USA, Vol. 83, Oct.-Nov. 1986, pp. 7508-7512, 8390-8394, 8779-8783.
van Hateren, Biol. Cybernetics 1992, "A theory of maximizing sensory information".
van Hateren, J. Comp. Physiology, 1992, "Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation".
Dan, Atick and Reid (1996), "Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory", The Journal of Neuroscience, Vol.16 (10) pp. 3351-3362
Atick, J. J. and Redlich, A. N. (1992). "What does the retina know about natural scenes?" Neural Computation 4:196-210, 1992
On efficient coding in fly vision:
Laughlin 1987: "Form and function in retinal processing", TINS, vol. 10, 1987.
Tuning curves, Population Coding
Fisher and Shannon informations:
S. Seung and H. Sompolinsky, PNAS 90 pp:10749-10753, 1993 (click here)
N. Brunel and J.-P. Nadal, Neural Computation, Vol. 10 issue 7 (October 1, 1998) pp. 1731-1757 (click here)
"Information theoretic approach to neural coding and parameter estimation: a perspective" (click here).


Class 7, Nov. 17: Networks: architectrures, rate models.

Presentation: slides (pdf)
Papers
Presentation: slides (pdf)
Papers

Class 8, Nov. 24: Networks of spiking neurons.

Presentation: slides (pdf)
Papers

Class 9, Dec. 1: Primary visual cortex

Presentation: slides (pdf)
Papers

Class 10, Dec. 8: Auditory cortex

presentation (ppt), presentation (pdf)


Class 11, Dec. 15: Hippocampus

presentation (pdf)
Papers


Class 12, Jan 5, 2012: Association cortex

presentation (pdf)
Papers


Class 13, Jan 12, 2012: Cerebellum

presentation (pdf)
Cerebellum: Papers

Basal ganglia

presentation (pdf)
Course material





Problem sets

Problem sets for the year 2011-2012 here




Archives, specific material for this Course:


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GENERAL REFERENCES

 

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.

 

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

A more 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, 1995)

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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