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
- Statistical structure of the trial-to-trial timing variability in synfire chains
- Internal state configures olfactory behavior and early sensory processing in Drosophila larva
- Bilateral alignment of receptive fields in the olfactory cortex points to non-random connectivity
- Supervised Deep Similarity Matching
- Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks
- Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
- A closer look at disentangling in β-VAE