227-1038-00L Neurophysics
| Semester | Spring Semester 2016 |
| Lecturers | J.‑P. Pfister, R. Hahnloser |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Catalogue data
| Abstract | The 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) |
| Objective | This 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.). |
| Content | 1. 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 |
| Lecture notes | Original research articles will be distributed. Specific pointers to textbooks will be provided. |
| Literature | Gerstner 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). |
| Prerequisites / Notice | Knowledge of standard methods in analysis, algebra and probability theory are highly desirable but not necessary. Students should have programming experience. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | J.-P. Pfister, R. Hahnloser |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| Mode of examination | oral 30 minutes |
| Additional information on mode of examination | Some visible efforts have to be made to solve the weekly defined tasks. In addition, homework must be orally presented during the exercise sessions. Teamwork is encouraged. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 227-1038-00 V | Neurophysics | 2 hrs |
| J.‑P. Pfister, R. Hahnloser | |||
| 227-1038-00 U | Neurophysics | 1 hrs |
| J.‑P. Pfister, R. Hahnloser |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
