Leveraging dynamical smoothness to predict motor cortex population activity

  • Awardees
  • Mark M. Churchland, Ph.D. Columbia University
  • Larry Abbott, Ph.D. Columbia University
  • John P. Cunningham, Ph.D. Columbia University
  • Liam Paninski, Ph.D. Columbia University

Animals often move in response to the senses. A dog follows a scent trail or a field mouse seeks cover after a hawk’s shadow passes overhead. Neuroscientists studying the sensory system have made great strides decoding how the brain translates sights and smells into neural activity. They give animals a specific stimulus — a pattern of stripes, say, or a specific chemical odor — and simultaneously measure the brain’s ‘code’ for that stimulus: i.e., how neural activity depends upon the stimulus. However, this approach has largely failed for the brain’s output side, the motor system. Despite decades of work, neuroscientists still struggle to describe how neural activity in the brain relates to the movements being generated. The number of neurons in the brain’s motor areas vastly exceeds the number of muscles, meaning that many different patterns of neural activity could be responsible for the same movement. This makes it potentially impossible to find a unique, simple code describing neural activity in terms of movement. Our goal is to turn this conundrum into an opportunity. Of all the patterns of neural activity that could produce a given movement, the brain must pick one, and this ‘choice’ may be illuminating regarding the basic principles at play. Using a combination of experiments and computer modeling, we aim to describe the choices made by the brain’s movement-generating system, and to decipher the principles behind those choices. We will train monkeys to use their arms to control a pedal-like device and navigate a virtual world for juice reward. At the same time, we will record the activity of neurons in the motor cortex and muscles in the arms. Pedaling movements have predictable properties — they are smooth and occur in cycles of varying speeds. Yet pedaling is driven by complex patterns of muscle activity that the brain must precisely create and control to ensure smooth movement. By comparing muscle activity to neural activity across many such movements, we can employ mathematics and computer optimizations to ask why the brain ‘chooses’ the observed patterns of neural activity. Neural network models can then be used to ask whether and why those choices are ‘good’ choices — i.e., do they allow the motor system to function better under challenging circumstances? Results will be compared between monkeys and mice. Indeed, we anticipate our approach will offer general insights into motor systems in any animal, including humans.

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