Communication between neural populations: circuits, coding, and behavior

  • Awardees
  • Marlene Cohen, Ph.D. University of Pittsburgh
  • Brent Doiron, Ph.D. University of Pittsburgh
  • Chengcheng Huang, Ph.D. University of Pittsburgh
  • Adam Kohn, Ph.D. Albert Einstein College of Medicine of Yeshiva University
  • Christian Machens, Ph.D. Fundação D. Anna de Sommer Champalimaud e Dr. Carlos Montez Champalimaud
  • Kenneth Miller, Ph.D. Columbia University
  • Alexandre Pouget, Ph.D. University of Geneva
  • Byron Yu, Ph.D. Carnegie Mellon University
Year Awarded


Every time we open our eyes, we take in far too much visual information for our brains to fully process. To cope with this issue, our brains have learned to pick out the most important details in a visual scene and quickly decide how to act on that information. That process requires multiple components — some parts of the brain encode sensory information, some parts retain this information while making a decision, and some parts decode sensory and cognitive information in order to act upon it. We want to determine how parts of the brain encode information, relay it to one another, and decode it, which is fundamental to proper brain function. We will record the electrical activity in the brains as animals perform a visual decision-making task. Because we can simultaneously record from many neurons in different parts of the brain at once, we can analyze how these different regions communicate with one another. For example, brain activity patterns vary even when perceiving the same stimulus or making the same movement. How does this variation influence how one part of the brain communicates to another? How does communication change depending on the internal state of the animal or the nature of the task? Do these changes enhance learning or other cognitive functions? We will then build computer models of neural networks in the cortex and compare those models to our data. Our models can simulate how brain areas process information before passing it along and how different processing strategies would affect transmission across the brain. We can then compare our predictions to the data observed in our recordings. We anticipate these fundamental discoveries will be broadly applicable to any species, including humans.

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