National course in Mathematical Neuroscience

The 5 study-point course FYS386 Mathematical Neuroscience will be given at the Norwegian University of Life Sciences (UMB) at Ås this Winter/Spring. It is intended to be an introductory course in Computational Neuroscience with specific emphasis on cortex modeling at the single-cell and network level. This course has been given in earlier years in a more modest colloquia-style fashion, but due to funding from the Norwegian node of the International Neuroinformatics Coordinating Facilty a full course with lectures and exercises will now be offered.

To make it possible for people outside the Oslo region to attend the course, we plan to have a "block-teaching" set-up where the students meet at UMB for two days at a time. This will be repeated 3 times through the semester. The students must of course both read the curriculum and work on the exercises at their home institutions in between.

Note that we aim to grant some travel stipends for people outside the Oslo area.

The course is intended for people with a mathematically oriented background (typically physics, mathematics, computer science) with basic programming skills in Matlab or Python.

If you are interested in taking this course, please send an email to Tom Tetzlaff (tom.tetzlaff(at)umb.no), and please visit the course web-page (where you can find information about registration etc.).

More detailed information about the course:

  • Course name: FYS386 Mathematical Neuroscience
  • Location: Institutt for matematiske realfag og teknologi, UMB
  • Teacher: Dr. Tom Tetzlaff, post.doc. UMB
  • Study points: 5
  • Time period: February-May 2008, (preliminary) dates: 14./15.02., 13./14.03., 17./18.04. + 23.05. (oral exam)
  • Language: English
  • Study form: Blocks of lectures + exercises at UMB
  • Curriculum (tentative):
    • 1st block: Introduction + Spike-train statistics
      • cortical anatomy
      • neuron morphology and electrophysiology
      • neuronal signal levels
      • spike-train statistics (firing rates, variability, correlations, spectra, etc.)
    • 2nd block: Neuron models
      • neuroelectronics
      • modeling levels: multicompartment vs. point neurons
      • Hodgkin-Huxley theory
      • 2D neuron models
      • integrate-and-fire models
      • rate models
    • 3rd block: Network models
      • feed-forward networks
      • mean-field theory of recurrent random networks:
      • population rate models
      • stochastic network dynamics
      • Hopfield networks, associative memory
  • Exam: Oral exam in May 2008
  • Literature
    • Gerstner & Kistler, Spiking neuron models: single neurons, populations, plasticity, Cambridge University Press 2002 [link]
    • Dayan & Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press 2005 [link]