# Teaching

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### Neurophysics 2016 course

Lecture 1: Dynamical systems.
[Exercise 1],
[Solution 1],
[See Ch4 in Gerstner et al. ]
[Lecture Docs 1]

Lecture 2: Lyapunov functions.
[Exercise 2],
[Solution 2],
[Lecture Docs 2]

Lecture 3: BCM learning rule.
[Exercise 3],
[Solution 3],
[Lecture Docs 3]
[Matlab Code 3]

Lecture 4: Bayesian inference
[Exercise 4],
[Solution 4],
[Lecture Docs 4]

Lecture 5: Factor Analysis
[Exercise 5],
[Solution 5],
[Lecture Docs 5]
[Matlab Code 5]

Lecture 6: Kalman filter
[Exercise 6],
[Solution 6],
[Lecture Docs 6]
[Matlab Code 6]

Lecture 7: Hebbian Learning and Oja's rule
[Exercise 7],
[Solution 7],
[Lecture Docs 7]
[Matlab Code 7]

Lecture 8: Fokker-Planck equation
[Exercise 8],
[Solution 8],
[Lecture Docs 8]

Lecture 9: Kushner equation
[Exercise 9],
[Solution 9],
[Lecture Docs 9]
[Matlab Code 9]

Lecture 10: Particle Filtering
[Exercise 10],
[Solution 10],
[Lecture Docs 10]
[Matlab Code 10]

Lecture 11: Generalised linear model
[Exercise 11],
[Solution 11],
[Lecture Docs 11]

Lecture 12: Learning with the generalised linear model
[Exercise 12],
[Solution 12],
[Lecture Docs 12]