227-1038-00L  Neurophysics

SemesterFrühjahrssemester 2016
DozierendeJ.‑P. Pfister, R. Hahnloser
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch


KurzbeschreibungThe focus of this course is on statistical approaches in neuroscience. The emphasis of this course is on both the mathematical methods as well as their applications to the modelling and analysis of electrophysiological recordings.

This course is taught by Prof. Jean-Pascal Pfister (2 lectures will be given by Prof. Richard Hahnloser)
LernzielThis class is an introduction to computational neuroscience research for students with a strong background in quantitative sciences such as physics, mathematics, and engineering sciences. Students who take this course learn about mathematical methods that are widely applied in neuroscience. In particular, they will learn about graphical models, dynamical systems, stochastic dynamical systems as well as probabilistic filtering. Those methods will be applied in the context of single neuronal dynamics, synaptic plasticity, neural network dynamics. Part of the exercices will be performed in Matlab (Mathworks Inc.).
Inhalt1. Introduction to dynamical systems
a. single neuron models (Fitzug-Nagumo model)
b. synaptic plasticity (Hebbian learning, Oja's rule, BCM learning rule)

2. Graphical models
a. Bayesian inference, cue combination tasks
b. parameter learning (Expectation-Maximisation algorithm)

3. Stochastic dynamical systems (Fokker-Planck equation)

4. Probabilistic filtering
a. Kushner equation
b. Kalman-Bucy filter
c. particle filter

5. Point emission processes (spiking neurons)
a. Spiking network dynamics (Generalised Linear Model - GLM)
b. Learning with the Generalised Linear Model, link to Spike-Timing dependent plasticity
c. Reward-based learning
SkriptOriginal research articles will be distributed. Specific pointers to textbooks will be provided.
LiteraturGerstner et al. (2014). Neuronal Dynamics - From single neurons to networks and models of Cognition
Barber (2012). Bayesian Reasoning and Machine Learning
Rieke et al. (1999) Spikes: Exploring the neural code
Bain, A., & Crisan, D. (2009). Fundamentals of stochastic filtering (Vol. 3).
Voraussetzungen / BesonderesKnowledge of standard methods in analysis, algebra and probability theory are highly desirable but not necessary. Students should have programming experience.