|Lesgever(s)||Erik De Schutter|
Deze cursusinformatie is bedoeld om de student te ondersteunen bij het verwerken van de leerstof
Bij aanvang van dit opleidingsonderdeel dient de student over de volgende competenties te beschikken:
Actieve beheersing van :Passieve beheersing van :
- 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.
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.
to Physics and Engineering, modern Neurosciences reached a level of
maturity where theoretical analysis and mathematical modeling are
standard tools, complementing experimental methods.
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
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.
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).
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.
Eigen werk: OefeningenOpdrachten:IndividueelScriptie: Individueel
Examen: Mondeling zonder schriftelijke voorbereiding
Permanente evaluatie: OpdrachtenEnkel een evaluatiemoment in de eerste zittijd, geen tweede examenkans mogelijk
Schriftelijk werkstuk: zonder mondelinge toelichting
Portfolio: met mondelinge toelichting
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.
D, Graham B, Gillies A, Willshaw, (2011) Principles of Computational
Modelling in Neuroscience, Cambridge University Press.
7. Contactgegevens en begeleiding
(+)laatste aanpassing: 02/02/2012 00:43 michele.giugliano