Supercomputer Replicates Brain Circuitry to Direct a Prosthetic Arm
By applying a novel computer algorithm to mimic how the brain learns, a team of researchers—with the aid of the Comet supercomputer based at the San Diego Supercomputer Center (SDSC) at the University of California, San Diego (UCSD), and the SDSC's Neuroscience Gateway—has identified and replicated neural circuitry that resembles the way an unimpaired brain controls limb movement. The research, published in the March-May 2017 issue of the IBM Journal of Research and Development, lays the groundwork to develop realistic "biomimetic neuroprosthetics"—brain implants that replicate brain circuits and their function—that one day could replace lost or damaged brain cells or tissue from tumors, stroke, or other diseases.
"In patients with motor paralysis, the biomimetic neuroprosthetic could be used to replace the deteriorated motor cortex where it could interact directly with healthy brain premotor regions, and send commands and receive feedback via the spinal cord to a prosthetic arm," said W.W. Lytton, MD, a professor of physiology and pharmacology at State University of New York (SUNY) Downstate Medical Center (Downstate), Brooklyn, New York, and the study's principal investigator.
The researchers relied on several concepts inspired by biology to create a more realistic artificial neural network that allows the motor cortex to learn to direct a virtual arm—consisting of eight bones, seven joints, and 14 muscle branches—to a specified target. The biomimetic model in question involved more than 8,000 spiking neurons and about 500,000 synaptic connections. The main component consisted of primary motor cortex microcircuits based on brain activity mapping, connected to a circuitry model of the spinal cord and the virtual arm.
"We argue that for the model to respond in a biophysiologically realistic manner to ongoing dynamic inputs from the real brain, it needs to reproduce as closely as possible the structure and function or actual cortical cells and microcircuits," said Salvador Dura-Bernal, PhD, a research assistant professor in physiology and pharmacology with Downstate and the paper's first author.
The researchers trained their model using spike-timing dependent plasticity (STDP) and reinforcement learning. The process refers to the ability of synaptic connections to become stronger based on when they are activated in relation to each other, meshed with a system of biochemical rewards or punishments that are tied to correct or incorrect decisions. In this case, the reward signal is based on the computer model's ability to control how close a virtual hand comes to a target. If the hand got close to the target, synapses generating that movement were rewarded; if the hand was further away, those synapses were punished.
Future studies will focus on developing more realistic models of the primary motor cortex microcircuits to help understand and decipher how information is encoded and transmitted in the brain.
Editor's note: This story was adapted from materials provided by UCSD.
Overview of biomimetic neuroprosthetic system. Left to right: Information about what target to reach can be gathered from electrodes in the brain. This modulates ongoing activity in the biomimetic cortical and spinal cord models that then drives the virtual arm, which is then mirrored by the robot arm. Right to left: Haptic feedback could then be delivered back in the other direction so that the user could feel what is being touched. Reproduced with permission from Dura-Bernal et al. 2017 (IBM Journal of Research and Development).