Dynamical computation in populations — analysis and theory

  • Awardees
  • Maneesh Sahani, Ph.D. University College London

The brain has a remarkable ability to choose appropriate actions in the face of an ever-changing environment. Even when sensory input is completely new — such as being in a new environment — animals are able to navigate successfully, respond to threats and capture prey. This ability depends on integrating sensory input with past experience to make decisions and implement behavior. Neuroscientists have long sought to understand how the brain makes complex decisions in uncertain environments. Recent technical advances have produced huge amounts of brain activity data recorded as animal perform these types of tasks, yet neuroscientists have developed relatively few new theories to explain these data. We have established a collaborative group to develop mathematical descriptions of brain activity called ‘dynamical neural models.’ Dynamical neural models help explain how the activity of hundreds or even millions of neurons represent internal variables — a sound, the position of a limb during a movement, the desire to move in the first place, or something else. We will study how groups of neurons pass messages to each other to create these representations, as well as how the brain learns to form them and use them for decisions and actions. Our work will provide much-needed theories of brain function to help interpret the large amounts of data generated by neuroscientists.

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