SS 2018 WS 2017
SS 2017
SS 2016 WS 2016
Department of Chemistry
open physics
KVL / Klausuren / MAP 1st HS: 17.04  2nd HS: 05.06  sem.br.: 24.07  begin WS: 14.10

4020170026 Neural Noise and Neural Signals  VVZ 

VL
Wed 13-15
weekly nV or digital (0) Benjamin Lindner
UE
Mon 13-15
weekly nV or digital (0) Benjamin Lindner

Digitaler Kurs

Aims
Aspects of randomness in neural activity and information processing can be successfully analyzed in terms by stochastic models. This course gives an introduction to the models and measures of neural noise (or 'variability' as it is more often called) and should enable the student to follow the current literature on the subject on his/her own. To this end, some key concepts from nonlinear dynamics, stochastic processes, and information theory are outlined. Then a number of basic problems (see below) is addressed; here, the main emphasis is given to analytically tractable models, but simulation techniques are explained as well. As an outlook some more involved problems (ISI statistics under correlated ('colored') noise, with subthreshold oscillations, or with adaptation, stimulus-induced correlations) are sketched at the end of the course.
Structure / topics / contents
Spontaneous activity and information transmission in models of single nerve cells

Contents include: Key concepts from nonlinear dynamics (bifurcations, fixed points, manifolds, limit cycle), stochastic processes (Langevin and Fokker-Planck equations, Master equation, linear response theory), information theory (mutual information and its lower and upper bounds), point processes (Poisson process; renewal vs. nonrenewal point process). Neural noise sources and how they enter different neuron models, the diffusion approximation of synaptic input or channel fluctuations by a Gaussian noise, measures of spike train and interval variability and their interrelation, Poisson spike train: entropy & information content, one-dimensional stochastic integrate-and-fire (IF) neurons: spontaneous activity, response to weak stimuli & information transfer, different forms of stochastic resonance in single neurons and neuronal populations, multidimensional IF models: subthreshold resonances, synaptic filtering & spike-frequency adaptation, effect of nonrenewal behavior of the spontaneous activity on the information transfer, outlook: stimulus-driven correlations; networks of stochastic neurons.
Assigned modules
P23.3_2010
Amount, credit points; Exam / major course assessment
3 SWS, 6 SP/ECTS (Arbeitsanteil im Modul für diese Lehrveranstaltung, nicht verbindlich)
oral examn
Other
Lecture takes place in the lecture hall of house 6,
Philippstr. 13, 10115 Berlin (which is the main building of the Bernstein Center for Computational Neuroscience Berlin). The tutorial will be in the same building in the seminar room 114.
Contact
Prof. Lindner NEW 15 3'412 (oder Campus Nord, Philippstr. 13, Haus 2, Raum 1.17
Quod vide:
http://people.physik.hu-berlin.de/~neurophys/neusig/index.html
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