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 have openings for graduate students and postdoctoral fellows.
Recent Publications
- A closer look at disentangling in β-VAE
- Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks
- 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