Animal behavior typically involves interactions among networks of large numbers of interconnected neurons, but experimental techniques are limited to the direct measurement of relatively small numbers of neurons. My laboratory uses computational modeling to bridge the gap between single-neuron measurements and hypothesized network function. We study a wide variety of systems and seek to address questions ranging from cellular and network dynamics to sensory coding to plasticity and learning. These include the accumulation and storage of information in working memory, and the circuit basis of reinforcement learning in motor and cognitive tasks.

One major area of research is understanding the mechanisms by which networks accumulate and store information in working memory. Recent projects seek to build cellular-resolution models of how multiple brain regions work together to store working memories. In one set of projects, we are combining data from cellular resolution recordings and perturbations of activity with detailed anatomy from connectomics to determine the mechanisms by which signals controlling the movements of the eye are mathematically integrated and stored. In a second set of projects, we are modeling how multiple cortical and subcortical structures work together to govern the accumulation of evidence in a decision making task. In a third set of projects, we are using the eye movement system to study the circuitry underlying cerebellar-mediated learning. Other projects in the lab include studying the cortical circuitry underlying binocular vision and the cortico-striatal circuit dynamics mediating songbird learning.