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Opleidingsonderdelen 2012-2013  
    
Computational neuroscience
Studiegidsnr:2036FBDBMW
Vakgebied:Biomedische wetenschappen
Semester:1e semester
Contacturen:45
Studiepunten:6
Studiebelasting:168
Contractrestrictie(s):Geen contractrestrictie
Instructietaal:Engels
Examen:1e semester
Lesgever(s)Michele Giugliano
Erik De Schutter

 

Deze cursusinformatie is bedoeld om de student te ondersteunen bij het verwerken van de leerstof


1. Aanvangscompetenties

Bij aanvang van dit opleidingsonderdeel dient de student over de volgende competenties te beschikken:
Actieve beheersing van :
  • Engels
Passieve beheersing van :
  • Engels
  • Een algemene kennis van het gebruik van een PC en het internet

Notie hebben van de basisbegrippen van:
Knowledge of MATLAB is welcome.
OCTAVE, a free alternative to MATLAB is available here.

Specifieke aanvangscompetenties voor dit opleidingonderdeel:
  • Basic math, algebra and calculus
  • Basic Physics
  • Strong interests for interdisciplinary topics
  • Basic cellular biology

Relation to other courses
Knowledge of the material presented in ‘Cellular and Molecular Neuroscience’ (1M BMW) and ‘Systems Neuroscience’ (1M BMW) is a plus but is not compulsory.



2. Eindcompetenties

In this course, we introduce a selection of topics in Computational Neuroscience, in search for a mechanistic dissection on how the mammalian nervous system work. Students will become knowledgeable in this domain.

Particular emphasis is placed in introducing the language of Cellular Electrophysiology and Biophysics , as the great discoveries were made in these domains where the availability of solid theoretical and computational tools revealed to be extremely fruitful. Students will become fluent in this language and skills.




3. Inhoud

Similar to Physics and Engineering, modern Neurosciences reached a level of maturity where theoretical analysis and mathematical modeling are standard tools, complementing experimental methods.

From molecular investigations, to cellular-, microcircuit-, network-, and system-level understanding of the brain (dys)functions, Computational Neuroscience aims at bridging the multifaceted features of these levels of organization and at providing an interdisciplinary common language. Identifying unifying concepts and common principles are among the ultimate goals of this discipline. Simulating complex experiments, testing the consequences of hypotheses, and performing manipulations that would be impossible to carry out in real experiments with today methods, further represent one of the most successful contribution of computational neurosciences, towards a quantitative understanding of the concerted working of molecules, cells, networks and systems.


Computational Neuroscience aims to explain and to model quantitatively the electrical properties of individual biological nerve cells, as well as of ensembles of interacting neurons. By the use of simple (bio)physical principles, reduced mathematical descriptions, and strong links to actual experimental measurements, this discipline attempts at relating the structure of the central nervous system to its function.
This endeavor is approached at different levels: from the ion-channels to the cell, from the microcircuitry to the system-level, as well as from learning to behavior and cognition.

Theoretical contributions to Neuroscience, Neurophysiology and Neurobiology date back to the beginning of the last century (e.g. when Lapique conceived in 1907 the first model of an neuron; when in 1950s, Hodgkin and Huxley described a system of differential equations to capture the emergence of the nerve impulse; or when in the 1960s Rall proposed a quantitative model - based on the cable equation formalism - to describe the distributed electrical properties of a nerve fiber; or when in Marr proposed a computational theory for the  Cerebellum). It is interesting to note that mathematical tools and conventional physics concepts  have been widely employed historically, within neurobiology (e.g. Mandelbrot's use of random-walk models for capturing the electrical activity of single neurons). Other examples are represented by diffusion Ornstein-Uhlenbeck processes and first-passage problems, together with Fokker-Planck equations and formalisms,  employed  to describe the statistics of neuronal ensembles or of a large collection of voltage-gated ion-channels (e.g. each described as a Markov's chain).

Over the past 20 years however, Theoretical Neuroscience further developed considerably and matured. It has evolved in an integral component of virtually every (neuro)scientific meeting and major university departments, across the globe. Today, this discipline attracts engineers, physicists, mathematicians, psychologists,  biologists, and it is growing at an incredible pace, with global funding initiatives such as the IBM-sponsored BlueBrain Project or the FP7 Flagship candidate initiatives such as the HumanBrain Project. The catalyst events for such an evolution have been identified by some people in two recent events: 1) the popularization of the backpropagation algorithm for training artificial neural networks in machine learning (Rumelhart and McClelland, 1986), and the elegant way proposed by Amit, Gutfreund, and Sompolinsky (Amit et al., 1985) on how a memory model proposed by Hopfield (1982) could be analyzed using methods of statistical physics, originally designed for spin glasses.

The course offered today at the University of Antwerp, currently to students of the Biomedical Sciences and Computer Science Curricula, is designed to provide an introduction to the field of Computational Neuroscience and some hands-on experience in mathematical modelling and computer simulation of neurons, synapses, and networks, with an emphasis towards biophysics and the meaning of biological computation.
Depending on the audience, the course can be shaped with different depths, and more or less references to machine learning or to electrophysiology and biophysics.
 


4. Werkvormen
Contactmomenten:
  • Hoorcolleges
  • Oefeningensessies
  • Seminaries

  • Eigen werk:
  • Oefeningen
  • Opdrachten:Individueel
  • Scriptie: Individueel

  • Excursie
    Projectwerk:
  • Individueel

  • Projectwerk:
  • In groep



  • 5. Evaluatievormen

    Examen:
  • Mondeling zonder schriftelijke voorbereiding

  • Permanente evaluatie:
  • Opdrachten
  • Enkel een evaluatiemoment in de eerste zittijd, geen tweede examenkans mogelijk

  • Schriftelijk werkstuk:
  • zonder mondelinge toelichting

  • Portfolio:
  • met mondelinge toelichting

  • Presentatie

    6. Studiemateriaal

    6.1 Noodzakelijk studiemateriaal

    Research and study material will be given or indicated by the lecturer(s) during the course.

    • Longstaff A, "BIOS Instant Notes: Neuroscience", 3rd edition, Garland Science.
    • Abbott LF, Dayan P (2001) Theoretical Neuroscience. Cambridge, MA: MIT Press.



    6.2 Facultatief studiemateriaal

    Het volgende studiemateriaal kan vrijblijvend bestudeerd worden.

    • Bear, Connor, Paradiso's for the biological introduction (chapter 1-4) 
    • Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation. Reading, MA: Addison-Wesley.
    • Sterratt D, Graham B, Gillies A, Willshaw, (2011) Principles of Computational Modelling in Neuroscience, Cambridge University Press.





    7. Contactgegevens en begeleiding

    Email: michele.giugliano_AT_ua.ac.be

    (+)laatste aanpassing: 23/09/2012 11:16 michele.giugliano