KVL / Klausuren / MAP 1.HS: 08.04  2.HS: 27.05  Zw.Sem.: 15.07  Beginn WS: 14.10

4020190120 Neural Noise and Neural Signals (B. Lindner)  VVZ 

Mi 11-13
wöch. nV or digital (0) Benjamin Lindner
Mo 13-15
wöch. 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
Überblick zu theoretischen Modellen der Neurophysik, die die spontane Aktivität 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
Umfang, Studienpunkte; Modulabschlussprüfung / Leistungsnachweis
3 SWS, 6 SP/ECTS (Arbeitsanteil im Modul für diese Lehrveranstaltung, nicht verbindlich)
muendliche Pruefung
Die Vorlesung findet im Hörsaal im Haus 6, Philippstr.13 (10115 Berlin) statt (im Bernstein Zentrum for Computational Neuroscience). Die Übung wird im selben Gebäude im Seminarraum 114 abgehalten.
Prof. Lindner NEW 15 3'412 (oder Campus Nord, Philippstr. 13, Haus 2, Raum 1.17
Siehe auch:
Moodle link:
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