Analog chip could be Rx for spinal cord injury
Published: Sep 28, 2007There’s a reason that a broken neck or back is considered to be one of the most tragic of injuries. If the spinal cord snaps, the brain loses its ability to communicate with the rest of the body, and the limbs to talk to each other. What most people don’t realize is that when it comes to locomotion, the second problem is actually worse than the first. The chicken with its head cut off can still run around, thanks to its spinal cord: The brain gave the signal to get going, then became superfluous to requirements. But if the limbs can’t “speak” to each other to coordinate, then walking is impossible.
Researchers at Johns Hopkins University (JHU; Baltimore) saw a way of getting around the problem. It turns out that the coordinated movements of limbs in all sorts of animals (including chickens) are produced by a central pattern generator (CPG). Sensors and actuators feed signals into the neurons of the spinal cord and then respond to the output. Because of the cyclical nature of walking, the spinal cord neurons learn to coordinate the inputs and outputs to produce a regular pattern: they become a CPG as the creature learns to walk. So, to give locomotion to an animal with a severed spinal cord, you need to reproduce this neural process.
If you could do so with an embedded chip, the researchers reasoned, you could enable walking at the flip of a switch.
Now they’ve shown that it really works. In a recent experiment with colleagues at the University of Alberta, Edmonton, they used a chip with analog neurons to control the walking of a temporarily paralyzed cat. Not only were signals from the chip used to stimulate the muscles, but the movement of the limbs was detected and fed back into the artificial neural network. The resulting movement might not have been completely natural, but it proved the concept. And this solution, unlike a more brute-force digital approach, has the potential of actually being implantable in the medium term.
Reggie Edgerton, a professor at the Department of Physiological Science and Neurobiology, University of California at Los Angeles, studies the neural control of movement and neuromuscular plasticity (adaptability and learning). He sees the new work as a step forward: “It provides a compact device that not only can stimulate the muscle but has some ability to modulate the stimulation amplitudes based on kinetic and kinematic feedback of the limbs.” The difficulty of what the JHU accomplished should not be underestimated, he said. “Perhaps the most important point from the present data is the suggestion of some success in proof of concept of recording sensory information, processing it, and then generating a reasonably successful adapted activation pattern of specific muscles.”
The neuromorphic approach
Ralph Etienne-Cummings, the JHU associate professor who has been in charge of the electronics work, has been working with CPG-based locomotion for several years. With his colleague Tony Lewis at Iguana Robotics (Mahomet, Ill.), he showed back in 2000 that a central pattern generator can be used to efficiently control walking in engineering as well as nature. Together, Lewis and Etienne-Cummings built a small robot: just a pair of legs driven at the hip. The knees were left to move freely, swinging forward under their own momentum like pendulums.
Locomotion was simple. The analog CPG chip designed by Etienne-Cummings would produce a burst of spikes that would drive the left/right hips forward/back. Position sensors on the hips would send spikes to the chip when their extremes had been reached, which would modify the output of the CPG and cause the left/right hips to start moving back/forward instead. Essentially, the sensors helped to feed in information about the real-time physics of the legs into the CPG, and it in turn coordinated the actions of the legs.
This particular CPG chip worked through the charge and discharge of an analog capacitor, so incoming spikes supplied by the extreme hip position sensors had the effect of either charging the CPG faster (in the first phase) or allowing it to discharge more slowly than it would have otherwise. Because that would change the period of the CPG, the next ‘extreme’ spikes would hit at a different part of the cycle, altering its pattern again. However, eventually, the CPG pattern would converge to that of the sensor spike pattern (a process known as entrainment), and the walking pattern would be set. Thus, as soon as one leg was fully extended, the other hip would start to push forward, producing a gait that exactly matched the physics of the legs. The researchers were also able to make the legs step over obstacles by adding a camera, appropriately converting its output into spikes, and feeding those into the CPG.
