Our research lies at the intersection of neuroscience and computer science. We develop new machine learning methods to map the structure and function of neural circuits.
Recent technological advances in light and electron microscopy, optogenetics, single cell RNA sequencing, and more allow us to measure and perturb neural networks in unprecedented detail. These new data can be used understand how the structure of a neural circuit gives rise to its function -- how networks of neurons are connected, how cells are organized into cell types, and what activity and computation they produce. Our lab develops machine learning algorithms to map neural connectivity, and statistical models to characterize neural activity and to relate activity to connectivity. Check out some of our lab's collaborative work on GitHub.
Projects currently underway include:
- Using deep neural networks and variational autoencoders to predict the spiking activity of neurons, infer their connectivity in vivo, and understand how neurons integrate their synaptic inputs.
- Building statistical models of neural activity and connectivity.
- Connectome driven computational models of neural circuits to understand their function.
- Discovering cell types and their basis, from single cell RNA sequencing data.