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 2013/14)

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
(Sonja Grün)

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; exercise sheet)
  • 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 processes
Exercise
(Tom Tetzlaff, Jochen Eppler; exercise sheet)
  • Python:
    • functions
  • generate Gamma process using functions
  • generate shot-noise process  
5. Single neuron models
(Tom Tetzlaff; lecture slides)
  • basics electrophysiology
  • LIF model
  • non-linear IaF models
  • numerical integration methods
Exercise
(Tom Tetzlaff; exercise sheet)
  • exact integration
  • implement LIF model
  • measure F-I curve for DC input
6. Single neuron models
(Tom Tetzlaff; lecture slides)
  • conductances
  • HH model
Exercise
(Tom Tetzlaff; exercise sheet)
  • implement Hodgkin-Huxley model
7. Single neuron models
(Tom Tetzlaff; lecture slides)
  • reduction of HH to 2D
  • Izhikevich model
  • MAT models
Exercise
(Tom Tetzlaff; exercise sheet)
  • implement MAT neuron model
  • compare type-I and type-II
  • compare resonator and integrator
8. Single neuron models
(Tom Tetzlaff; lecture slides)
  • multi-compartment models
  • firing-rate models
  • model classification
Exercise
(Tom Tetzlaff; exercise sheet)
  • phase portraits of 2D dynamical systems (nullclines, fixed points, vector field)
9. Analysis of neuronal activity: Spiking activities
(Sonja Grün)

Exercise
(Junji Ito)
  • Python:
    • file I/O (.npy), matplotlib
    • numpy array indexing
  • load spike data, raster plot
  • implement cross-correlation
  • compute PSTH
10. Analysis of neuronal activity: Spiking activities
(Sonja Gruen)

Exercise
(Junji Ito)
  • compute cross-correlogram
  • significance test
11. Analysis of neuronal activity: Local-field potentials
(Michael Denker)

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

Exercise
(Michael Denker)
  • - Python:
    • file I/O with matlab and h5py
  • combination of spikes and LFP
  • spike-field coherence
  • spike-LFP phase synchronization
13. Network models
(Moritz Helias)

Exercise
(Maximilian Schmidt, Michael Denker)
  • create random connectivity matrices
  • implement simulation of binary E-I network
14. Network models
(Moritz Helias)

Exercise
(Moritz Helias, Maximilian Schmidt)
  • NEST
  • 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: Allg. Verfuegungszentrum AVZ 2, room 1.011, Helmertsweg 3, RWTH Melaten, RWTH Aachen University
  • time: winter term 2013/14, Thursday's, 2:30pm-5pm
  • exam: written exam at the end of the course