ikeda
Tom Tetzlaff

Inst. of Neuroscience and Medicine (INM-6)
Computational and Systems Neuroscience

Research Center Jülich


 

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Introduction to Computational Neuroscience

(winter term 2012/13)

Description:

introductory course for
  • MS Physics (minor-field module "Biophysics")
  • MS Biology (supplementary module for advanced studies "Bioinformatics")
  • PhD students in Computational Neuroscience

Contents:

Course introduction
(Markus Diesmann)

1. Anatomy of the cortex
(Tom Tetzlaff; lecture slides)
  • macroscopic cortex structure
Exercise
(Jochen Eppler)

  • Python introduction:
    • editor, command line, ipython
    • variables, basic data types
2. Anatomy of the cortex
(Tom Tetzlaff; lecture slides)
  • microscopic cortex structure
  • cortical connectivity
Exercise
(Jochen Eppler)

  • Python introduction:
    • operators, flow control, modules
    • programming and debugging strategies
    • help, how to use google
3. Probabilistic description of neuronal signals
(Tom Tetzlaff; lecture slides)        
  • firing variability
  • neural codes
  • point processes
Exercise
(Tom Tetzlaff, Jochen Eppler, Sacha van Albada)
  • Python:
    • numpy basics, arrays
  • create artificial spike trains according to Poisson and gamma processes
  • characterize statistics of spike trains: inter-spike interval distribution, coefficient of variation, spike count distribution, Fano factor, bin-size dependence
4. Probabilistic description of neuronal signals
(Tom Tetzlaff; lecture slides)
  • relation between spike-count and interval statistics
  • correlation functions and spectra
  • shot-noise
Exercise
(Tom Tetzlaff, Jochen Eppler)
  • Python:
    • functions
  • generate shot-noise process
5. Analysis of neuronal activity: Local-field potentials
(Michael Denker)

Exercise
(Michael Denker, Emiliano Torre)
  • visualization of LFP activity
  • spectral analysis (power/coherence)
  • phase synchronization
6. Analysis of neuronal activity: Local-field potentials
(Michael Denker)

Exercise
(Michael Denker, Emiliano Torre)
  • Python:
    • File I/O, h5py, exception handling
  • combination of spikes and LFP
    • spike field coherence
    • spike-LFP phase synchronization
7. Analysis of neuronal activity: Spiking activity
(Junji Ito)

Exercise
(Junji Ito, Michael Denker)
  • load spike data from file
  • raster plot
  • PSTH
8. Analysis of neuronal activity: Spiking activity
(Sonja Grün)

Exercise
(Junji Ito, Emiliano Torre)
  • compute auto- and cross-correlations
  • significance test
9. Single neuron models
(Tom Tetzlaff; lecture slides)
  • basics electrophysiology
  • LIF model
  • non-linear IaF models
  • numerical integration methods
Exercise
(Tom Tetzlaff)
  • Python: scipy.integrate.ode
  • implement LIF model using exact integration
  • measure F-I curve for DC input
10. Single neuron models
(Tom Tetzlaff; lecture slides)
  • conductances
  • HH model
  • 2D neuron models
Exercise
(Tom Tetzlaff)
  • phase portraits of 2D dynamical systems (nullclines, fixed points, vector field)
11. Single neuron models
(Tom Tetzlaff; lecture slides)
  • Izhikevich model
  • MAT models
  • multi-compartment models
  • model classification
Exercise
(Sacha van Albada)
  • implement Hodgkin-Huxley model
12. Single neuron models
(Tom Tetzlaff; lecture slides)
  • firing-rate models
  • rate dynamics of LIF with strong synapses
Exercise
(Moritz Helias)
  • implement binary neuron model
13. Network models
(Moritz Helias)

Exercise
(Moritz Helias, Sacha van Albada)
  • create random connectivity matrices
  • implement simulation of binary E-I network
14. Network models
(Moritz Helias)

Exercise
(Moritz Helias, Jochen Eppler)
    • NEST intro
    • open questions

Literature:

  • Dayan & Abbott (2005), Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press (www)
  • Abeles (1991), Corticonics: Neural Circuits of the Cerebral Cortex, Cambridge University Press (www)
  • Izhikevich (2010), Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, MIT Press (www)
  • Gerstner & Kistler (2002), Spiking neuron models: single neurons, populations, plasticity, Cambridge University Press (www, free online version)
  • see also references in lecture material

Additional Information:

  • SWS: 3
  • ECTS credits: 5 ECT (after passing written exam and participation in exercises [protocols, oral presentations])
  • language: English
  • location: Institute of Biology, Mies-van-der-Rohe-Str. 15, UMIC Bldg., Room 305, RWTH Aachen University
  • time: winter term 2012/13, Thursday's, 3pm-5:30pm
  • exam: written exam at the end of the course