My goal is to understand the rules that neurons are using that can explain synaptic rearrangements and neural arbor formation. We think neurons operate by the same basic rules throughout the life of the animal and that structural changes are a primary mechanism of learning and memory in mammals. Although many factors influence neuronal cell behavior, our view is that gain and loss of synapses and branches are part of a core behavioral repertoire that is best characterized at the cellular level.
A major focus of study in the lab is the process of naturally occurring synapse elimination. This process is also known as synaptic competition. Using mice as a model organism, we observe how motor neurons compete to innervate neuromuscular junctions (synapses) in skeletal muscle. At birth, the connectivity pattern appears largely random: each motor neuron may contact nearly every fiber of a small muscle, and the many inputs to each junction are highly intermingled. Yet within 2 weeks, the connectivity pattern goes from being ‘all-to-all’ to completely non-overlapping. Each junction ends up being innervated by just one axon. Although the final connectivity pattern appears random with respect to where each motor neuron wins and loses in a muscle, we do see evidence of non-random relationships between motor axons during the elimination process. My current research is focused on developing a theoretical framework to explain how these relationships come about and extending this framework to explain how development may occur in the CNS.
I have a background in electrical engineering and computer science and, over time, have transitioned to doing basic biological research. I enjoy developing new methods to further the imaging-based research of the lab. I am also helping to develop open-source software for acquiring, viewing and analyzing large-scale image datasets.