We conduct theoretical research on how neural networks learn and compute. To that end, we work at the interface of computational neuroscience and machine learning. Our group is based at the FMI in Basel, Switzerland.
- Nonlinear transient amplification in recurrent neural networks with short-term plasticityI am pleased to share our new preprint: “Nonlinear transient amplification in recurrent neural networks with short-term plasticity” led by Yue Kris Wu in which we study possible mechanisms for transient Hebbian amplification.https://biorxiv.org/cgi/content/short/2021.06.09.447718v1 Hebbian assembliesContinue reading Read more »
- Paper: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuitsWe’re excited to see this published. A new take on spatial credit assignment in cortical circuits based on Richard Naud’s idea on burst multiplexing. A truly collaborative effort lead by Alexandre Payeur and Jordan GuerguievContinue reading Read more »
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