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.
- Preprint: Brain-Inspired Learning on Neuromorphic SubstratesI’m happy to share our new overview paper (arxiv.org/abs/2010.11931) on brain-inspired learning on neuromorphic substrates in (spiking) recurrent neural networks. We systematically analyze how the combination of Real-Time-Recurrent Learning (RTRL; Williams and Zipser, (1989)) withContinue reading Read more »
- Kris and Claire present exciting excitation-inhibition work at the Bernstein conferenceBoth Claire and Kris will present virtual posters about their modeling work in the olfactory system. If you are interested in learning more about what precise EI balance and transient attractor states have to doContinue reading Read more »
- Hiring: Information processing in spiking neural networksWe are looking for Ph.D. students to work on the computational principles of information processing in spiking neural networks. The project strives to understand computation in the sparse spiking and sparse connectivity regime, in whichContinue reading Read more »
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