Population dynamics across pairs of cortical areas in learning and behavior

The activity of neurons in the brain represents external sensory stimuli, internal cognitive states, and plans for upcoming motor behavior. Neurons communicate with each other through electrical impulses known as spikes. By measuring spikes from small groups of neurons, researchers have made tremendous progress toward building sophisticated, data-driven models of brain function. However, technical limitations have so far prevented this approach from scaling to hundreds, thousands, or millions of neurons acting together. We have proposed a new collaboration between experimental and theoretical laboratories to develop models of brain function based on data from hundreds of neurons recorded in multiple brain areas simultaneously. We will investigate how learning changes the neuronal representation of sensory and decision-related information in cortex. At the core of our work is a newly developed imaging technology that allows for recording the spiking activity of hundreds of neurons at once. We have also developed powerful new statistical tools to help us analyze this data. Working in the visual system of mice, we will address several key questions: How is visual motion encoded in multiple brain areas during decision-making? How is ongoing activity related to how the brain encodes sensory and decision-related information? How does this activity change over time during learning? Our research will shed light on how the brain encodes sensory information during decision making, and on how that encoding changes as we learn.