The mission of my lab is to understand the organizational principles that underlie information processing in neuronal circuits.

My lab works at the intersection of neurobiology, physics, and engineering. We combine functional and structural mapping of brain circuits with computational analysis to reverse-engineer how network connectivity gives rise to neural computation and behavior. This is some times called ‘functional connectomics’. We develop and use high-throughput, high-resolution imaging techniques, machine learning, and computational approaches to unravel the topology of neuronal networks. We aim to discover how neuronal and network function arise from circuit wiring in the rodent and Drosophila brain. Our work is guided by several key questions:

• What logic underlies neuronal network connectivity ?

• What network motifs are conserved and what differentiates brains -and brain regions?

• What are fundamental constraints on network behavior?

• How are such rules enforced during development?

We primarily use large-scale electron microscopy (EM), X-ray microscopy, and in vivo multi-photon calcium imaging to examine the structure and function of neurons and networks. Volumetric EM and X-ray imaging provides detailed structural information about cells and their connections. We can identify excitatory and inhibitory neurons and synapses, discover connectivity motifs, and analyze the nature of synaptic connections. The other key component of our approach is physiology – either optical imaging of activity sensors or electrophysiology. Ideally, the same cells are subjected to in vivo physiological recording and connectivity analysis. In this way we can unravel how wiring patterns enable neuronal computations. Additionally, we use genetic tools for labeling and manipulation; and quantitative modeling to understand our data and generate testable theories. Finally, we are devising approaches to bridge analysis of behavior with circuit structure and network computation. By bridging these levels of inquiry, our goal is to uncover the fundamental building blocks of neural networks.