KVL / Klausuren / MAP 1.HS: 13.04  2.HS: 25.05  Zw.Sem.: 18.07  Beginn WS: 10.10

4020150023 Neural Noise and Neural Signals  VVZ 

VL
Mi 11-13
wöch. nV or digital (0) Benjamin Lindner
UE
Mi 9-11
14tgl. nV or digital (0) Benjamin Lindner

Digital- & Präsenz-basierter Kurs

Lern- und Qualifikationsziele
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.
Gliederung / Themen / Inhalte
Spontaneous activity and information transmission in models of single nerve cells
Ueberblick zu theoretischen Modellen der Neurophysik, die die spontane Aktivitaet und Signaltransmission in Nervenzellen beschreiben.

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.
Zugeordnete Module
P23.3.2a P23.3
Umfang, Studienpunkte; Modulabschlussprüfung / Leistungsnachweis
3 SWS, 5 SP/ECTS (Arbeitsanteil im Modul für diese Lehrveranstaltung, nicht verbindlich)
muendliche Pruefung
Sonstiges
Die Veranstaltung findet im Hörsaal im Haus 6, Philippstr.13 statt.
Siehe auch:
http://people.physik.hu-berlin.de/~neurophys/neusig/index.html
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