Corticocortical Signaling Between Populations of Neurons

How the human brain’s nearly 100 billion neurons interact with each other to give rise to cognition, emotion, and action is a total mystery. Neurons are embedded in dense networks, with thousands of connections to other neurons. These networks have connections at both the local level, between neighboring neurons, and at the global level, as projections between brain areas. Yet even the basic principles of how all of this complicated networked activity leads to thought and behavior are unknown. As we unravel this mystery, understanding the interactions between brain regions will be critical. One roadblock has been that previous studies have focused on correlations between pairs of neurons or field potentials, each recorded in a different brain area. Yet neurons may also communicate at the level of groups, or populations. Emerging technology now allows us to measure the activity of hundreds of neurons in multiple brain areas simultaneously, and advances in statistical analysis are beginning to decipher this complex population-level activity. In our research, we will focus on this question by studying the visual system of monkeys. We will place electrodes in two brain areas involved in processing visual information, V1 and V2. Neurons in V1 send information about visual scenes to neurons in V2, complicated by some feedback connections as well, with V2 sending information back to V1. With help from sophisticated statistical modeling, we will determine what information is passed along, in which direction, and why. This will lead to experiments in which we compare brain activity between brain areas V1 and V4 while monkeys report decisions based on their visual percepts. By studying and modeling the flow of information across multiple brain areas, our results will provide insight not only into the visual system, but also into how any collection of brain areas cooperate to give rise to perceptions and decisions.

Adam Kohn, Albert Einstein College of Medicine

Byron Yu, Carnegie Mellon University