Our research areas are theoretical neuroscience and neuroscience-guided machine learning. We seek to uncover the algorithms of the brain and their implementation at the network and cellular levels.
We are currently looking for postdoctoral fellows to join the group.
Recent Publications
- Neuroscience-inspired online unsupervised learning algorithms
- A Spiking Neural Network with Local Learning Rules Derived From Nonnegative Similarity Matching
- Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics
- Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching
- Holography, Fractals and the Weyl Anomaly
- Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits
- Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?