Neuro-inspired Theory, Modeling and Applications

  • LogoIndex
    Our theoretical research is concerned with understanding and analytically describing various aspects of neural network dynamics. On the modeling side, we are mainly interested in functional networks (i.e., networks that do something that we consider useful), for which we take inspiration from both biology and AI research. An essential aspect of model functionality concerns robustness, since one of our goals is the embedding of functional networks in neuromorphic substrates and their application to real-world problems.

Our Research Interests


  • New article in Elife: how natural-gradient descent enable efficient synaptic plasticity!
  • Kreutzer2022
    "Natural-gradient learning for spiking neurons".
    See Publications

  • DEEP MINDS Podcast (in German) - Info
  • New contribution to eLife: Learning across three different global brain states!
  • Our group leader speaks his mind!
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    Read the Interview that Mihai gave to Uniaktuell - the online magazine of the University of Bern - on Science and Politics.

  • New Article in NeurIPS: Learning with slow neurons is not a problem anymore!
    This awesome work was selected for an oral presentation at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)!
  • LEnews
    "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons".
    See Publications - Press Release