Our broad Goal is to understand the organizational principles that underlie information processing in neuronal circuits.
Our work is guided by several key questions:
What rules underly 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) and in vivo multi-photon calcium imaging to examine the structure and function of neurons and networks. Serial EM provides us with detailed anatomical information about neurons and their connections. We can identify excitatory and inhibitory neurons and synapses, discover connectivity motifs, and analyze the organization 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 infer how patterns of connectivity shape neuronal computations. Additionally, we use genetic tools for labeling and manipulation; and modeling to explore the implications of our data and generate testable theories. Finally, we are devising approaches that will allow us to use behavior to bridge our understanding of circuit structure and network computation. By working across these modes of inquiry we aim to uncover the fundamental building blocks of functional networks.
Research Topics
Connectivity Motifs in Association Cortex
→ In this project we are investigating how connectivity in cortical networks supports cognitive function in association cortex. We first record functional activity via in vivo 2-photon calcium imaging while trained mice perform navigational tasks in virtual reality, then we reconstruct the same circuit volume using large-scale electron microscopy. In this way we can record both functional activity and network connectivity for populations of neurons. We can then analyze how local circuit connectivity relates to their functional activity during behavior, and begin to understand how the cortical network architecture enables computations important for behavior.
Circuit Mechanisms of Motor Control
→ Animals use their legs and arms for an incredible number of different behaviors (e.g. walking, jumping, fighting, foraging), but how does the nervous system know how to execute all these different movement patterns? Controlling dozens of muscles to precisely move the limbs through space is no simple task. We aim to characterize the connectivity of neural circuits that control the legs of the adult fruit fly Drosophila melanogaster. The fly has already enabled numerous influential discoveries thanks to its diverse genetic toolbox and strong stereotypy from animal-to-animal, and we aim to establish the adult motor circuit connectome as a powerful new resource for understanding motor circuit function.
Acquisition of Connectomic Datasets
→ Transmission electron microscopy (TEM) is an essential tool for studying cells and molecules. Recently, we have developed a tape-based, reel-to-reel TEM pipeline that combines automated serial sectioning with automated high-throughput TEM imaging. This acquisition platform provides nanometer-resolution imaging at fast rates (>30 Mpixels/s), allowing the reconstruction of cortical microcircuits and complete insect brains. This automation has made it feasible for lab members to collect impactful electron microscopy datasets in months rather than years.
Analysis of Connectomic Datasets
→ As we generate electron microscopy image data at ever-growing speeds, there is increasing need for highly-accurate automated methods to segment neurons, detect synapses, and broadly analyze these data. We collaborate with deep learning researchers to develop, train, and deploy deep-learning networks to segment EM datasets.