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Andreas Herz

The brain is one of the most complex biological systems. Understanding its fascinating dynamics and information processing strategies remains a great challenge. The background of my group in non-linear dynamics, stochastic processes, information theory and collective phenomena offers a broad spectrum of concepts and techniques for collaborations with experimental labs and to study how the brain solves hard computational problems. Using different model systems – from acoustic communication of grasshoppers to spatial navigation in rodents – we address questions such as: How do sensory systems integrate information over multiple time scales to perform complex pattern recognition tasks? How are external signals analyzed in real time despite the constant influx of new sensory information? How do neural systems handle the dilemma of “insulation versus interaction“ inherent to any distributed signal processing? We have also developed novel experimental approaches, e.g., to find the most relevant set of stimuli for a given neuron or to disentangle its intrinsic processing steps. The results of these investigations can be used to further our basic concepts of “computing“ and create novel paradigms that surpass the limits of traditional approaches. This double role of computational neuroscience has led to major advances in brain research and provides new connections to theoretical physics, psychology, computer science and various technical application domains.

Research interests

  • computational neuroscience and cellular biophysics
  • neural basis of spatial navigation
  • interaction of cell-intrinsic rhythms and large-scale oscillations
  • microscale biophysics and structure-function relation of dendritic spines
  • collective properties of neural networks

In the past, I have also worked extensively in the fields of applied mathematics, statistical physics, game theory,   theoretical immunology and olfactory learning.

Scientific approach

We use methods from the theory of dynamical systems and probability theory to analyze and model electrophysiological data from various local and international collaboration partners and to compare them with theoretical predictions.