Vertebrate Physiology

Years ago, scientists were able to link a monkey sitting in North Carolina with a robotic arm situated at MIT’s Touch Lab. This doesn’t seem all that remarkable until you notice that the control is entirely by thought. The monkey is manipulating the arm using a brain wave-machine interface that could be used to design prosthetics for humans. The technique they used allows large numbers of single neurons to be recorded separately, then combines their information using a computer coding algorithm. The scientists implanted the electrodes in multiple regions of the brain’s cortex, including the motor cortex from which movement is controlled. They then recorded the output of these electrodes as the animals learned reaching tasks, including reaching for small pieces of food.
Now they have used the same technique to have the monkey control a robot walking on a treadmill.

To determine whether it was possible to predict the trajectory of monkeys’ hands from the signals, the scientists fed the mass of neural signal data generated during many repetitions of these tasks into a computer, which analyzed the brain signals. In this analysis, the scientists used simple mathematical methods and artificial neural networks to predict hand trajectories in real time as the monkeys learned to make different types of hand movements.

“We found two amazing things,” said Miguel Nicolelis, associate professor of neurobiology at Duke. “One is that the brain signals denoting hand trajectory show up simultaneously in all the cortical areas we measured. This finding has important implications for the theory of brain coding, which holds that information about trajectory is distributed really over large territories in each of these areas even though the information is slightly different in each area.

“The second remarkable finding is that the functional unit in such processing does not seem to be a single neuron,” Professor Nicolelis said. “Even the best single-neuron predictor in our samples still could not perform as well as an analysis of a population of neurons. So this provides further support to the idea that the brain very likely relies on huge populations of neurons distributed across many areas in a dynamic way to encode behavior.”