Cerebral Basis for Volitional Movement and Cortical Neural Prosthetics

Andrew Schwartz
Andrew Schwartz
University of Pittsburgh, USA
15:20~16:10, November 22nd, 2013


A better understanding neural population function would be an important advance in systems neuroscience. The change in emphasis from the single neuron to the neural ensemble has made it possible to extract high-fidelity information about movements that will occur in the near future. This ability is due to the distributed nature of information processing in the brain. Neurons encode many parameters simultaneously, but the fidelity of encoding at the level of individual neurons is weak. However, because encoding is redundant and consistent across the population, extraction methods based on multiple neurons are capable of generating a faithful representation of intended movement. The realization that useful information is embedded in the population has spawned the current success of brain-controlled interfaces. Since multiple movement parameters are encoded simultaneously in the same population of neurons, we have been gradually increasing the degrees of freedom (DOF) that a subject can control through the interface. Our early work showed that 3-dimensions could be controlled in a virtual reality task. We then demonstrated control of an anthropomorphic physical device with 4 DOF in a self-feeding task. Currently, monkeys in our laboratory are using this interface to control a very realistic, prosthetic arm with a wrist and hand to grasp objects in different locations and orientations. Our recent data show that we can extract 10-DOF to add hand shape and dexterity to our control set. This technology has now been extended has been extended to patients


Research Interests:

  • Neural correlates of action: single neurons and neural populations
  • Cortical Physiology
  • Cortical-Muscular Activation
  • Skeletal Biomechanics
  • Visual Motor transformation
  • Arm reaching
  • Reach-to-grasp
  • Control of dexterity
  • Neural Prosthetics
  • Anthropomorphic Robotics
  • Neural Statistics
  • System Control


Awards and Honors:

  • Carnegie Science Award for Life Sciences, 2010
  • IBMISPS Pioneer in Medicine Award, 2010
  • Popular Mechanics Breakthrough Award, 2012