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Berlin Theoretical Neuroscience Running lecture

Stochastic Processes in Neuroscience

Participants should learn basic concepts, their theoretical foundation, and the most common models of stochastic processes used in computational neuroscience to model noisy neural systems. Participants will learn basic techniques to analyze the stochastic behavior of singles neurons and neural systems both qualitatively and quantitatively. Participants will also learn basic simulation techniques for stochastic neural systems and how to evaluate simulation output. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems.
Contents include:
Brownian motion and stochastic calculus, stochastic models for single neurons (stochastic Hodgkin-Huxley model, stochastic integrate-andfire models, random oscillators), coupled neurons with noise, synchronization, stochastic stability, stochastic neural fields, travelling waves.

Lecture

MSc, PhD
  • Maths: advanced
  • Programming: basic
  • Neurobiology: none

Technical University of Berlin

Bernstein Center for Computational Neuroscience Berlin

Stannat
Winter term / weekly/4 h per week / 6 ECTS
Visit the course website