Joao Couto, Ph.D.

Joao Couto is a postdoctoral fellow at the University of California, Los Angeles (UCLA), studying long-range communication between brain areas during decision-making. He does this by combining mouse behavior with optogenetics, calcium imaging and electrophysiology. During his Ph.D. program in Antwerp, Belgium, he developed closed-loop methods to interrogate the dynamic properties of neurons and used those to show that cerebellar Purkinje cells transition smoothly between distinct operation modes. He then moved to Leuven, where he studied visual circuits. He found that cell types in the visual thalamus are more susceptible to modulations by behavior and studied visuo-tactile interactions during virtual navigation. Joao develops tools to record and interact with neurons, behavior apparatus and computational resources which he releases in the spirit of open and reproducible science. Moreover, he is mentored in the optical imaging and electrophysiological recordings course (Paris, France), and will provide close support and guidance.

Principal Investigator: Anne Churchland

Fellow: Madison Scout Lansing

Project: The prevalence of many neurological disorders is sex-specific, a disparity that may be due to differences in brain development because of when symptoms first arise. This research project proposes to investigate sex-specific differences in neural processing in the context of a decision-making task. Cortical cells can be grouped into categories based on their gene expression during development and be targeted using transgenic tools. The research group uncovered differences in the activation of developmentally defined cell types, suggesting that cell types may take different roles in decisions. Yet, it is not known if the brains of different sexes have the same activation patterns. To address this gap in knowledge, this project will first train mice in visual and auditory decision-making tasks. Second, it will measure activity across the entire dorsal cortex in different developmental mouse lines and animals of different sexes. Finally, it will use machine-learning tools to uncover sex-specific differences in neural processing.