Göttingen Graduate School for Neurosciences, Biophysics, and Molecular Biosciences

Gütig, Robert, Dr.

Max Planck Research Group Leader


  • Undergraduate studies in Physics and Psychology, FU Berlin, University of Cambridge and Heidelberg University (1993-1999)
  • MPhil in Theoretical Pysics, University of Cambridge, UK (1997)
  • PhD in Computational Neuroscience with Ad Aertsen, University of Freiburg (1999-2002)
  • Postdoctoral training with Andreas Herz, Institute of Theoretical Biology, HU Berlin (2003-2005)
  • Postdoctoral training with Haim Sompolinsky, Interdisciplinary Center for Neural Computation, Hebrew University of Jerusalem, Israel (2005-2011)
  • Max Planck Research Group Leader, Theoretical Neuroscience (since 2011)




Major Research Interests


We use analytical and numerical modeling techniques to identify the computational principles underlying spike based information processing and learning in central nervous systems and to understand how these principles are implemented by biological processes. Specifically, we focus on the role of action potential timing in subserving sensory neuronal representations and computation as well as in controlling synaptic plasticity. Projects center around the recently developed tempotron family of spiking neuronal network models and cover a broad range of topics including mathematical analyzes of information processing in spiking neuronal networks, spike-based learning in single and multi-layer neuronal networks, sensory spike data analysis, temporal processing with short term synaptic dynamics, as well as applied development of visual and speech processing systems.



Homepage Department/Research Group
http://www.em.mpg.de/index.php?id=281



Selected Recent Publications


  • Gütig R (2014) To spike, or when to spike? Current Opinion in Neurobiology 25 134-139.
  • Gütig R, Gollisch T, Sompolinsky H, Meister M (2013). Computing complex visual features with retinal spike times. PLoS One 8 e53063.
  • Gütig R, Sompolinsky H (2009). Time-warp-invariant neuronal processing. PLoS Biology 7 e1000141
  • Gütig R, Sompolinsky H (2006). The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience 9 420-428
  • Gütig R, Aharonov R, Rotter S, Sompolinsky H (2003). Learning input correlations through non-linear temporally asymmetric Hebbian plasticity. Journal of Neuroscience 23 3697-3714
  • Gütig R, Rotter S, Aertsen A (2003). Analysis of higher-order neuronal interactions based on conditional inference. Biological Cybernetics 88 352-359
  • Gütig R, Aertsen A, Rotter S (2002). Statistical signi cance of coincident spikes: count-based versus rate-based statistics. Neural Computation 14 121-153
  • Betsch T, Plessner H, Schwieren C, Gütig R (2001). I like it but I don't know why: A value-account approach to implicit attitude formation. Personality and Social Psychology Bulletin 27 242-253
  • Gütig R, Eberlein C (1998). Quantum radiation from moving dielectrics in two, three, and more spatial dimensions. Journal of Physics A 31 6819-6838