Neuro-inspired theory, modeling and applications

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  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

• > Dynamics and Statistics of Spiking Neural Networks
  > Neural Computation
  > Plasticity and Learning
  
  

Highlights

• New article in Elife: how natural-gradient descent enable efficient synaptic plasticity!

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  "Natural-gradient learning for spiking neurons".
  See Publications
  
  
  

• DEEP MINDS Podcast (in German) - Info

• In the episode 7 of the DEEP MINDS Podcast: Laura Kriener and Julian Göltz talked about "AI Hardware of the Future: What is Neuromorphic Computing?"
  
  
  

• New article in Elife: Learning across three different global brain states!

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  "Learning cortical representations through perturbed and adversarial dreaming".
  See Publications
  
  

• 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)!
  

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  "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons".
  See Publications - Press Release
  
  

• > More to explore: News and Publications