Biomimetic Somatosensory Feedback through Intracorticalmicrostimulation

Spinal cord injury causes both paralysis and loss of sensation from the limbs. The past 15 years have seen remarkable advances in “Brain Machine Interfaces” (BMIs) that allow paralyzed persons to move anthropomorphic limbs using signals recorded directly from their brains. However, these movements remain slow, clumsy, and effortful, looking remarkably like those of individuals who have lost sensation from their arms due to peripheral neuropathy. Brain-controlled prosthetic limbs are unlikely to achieve high levels of performance in the absence of artificial sensory feedback. Early attempts at restoring somatosensation used intracortical microstimulation (ICMS) to activate somatosensory cortex (s1), requiring animals to learn largely arbitrary patterns of stimulation to represent two or three virtual objects or to navigate in two-dimensional space. While an important beginning, this approach seems unlikely to scale to the broad range of limb movements and interactions with objects that we experience in daily life. To move the field past this hurdle, we propose to replace both touch and proprioception by using multi- electrode ICMS to produce naturalistic patterns of neuronal activity in S1 of monkeys. In Aim 1, we will develop model-optimized mappings between limb state (pressure on the fingertip, or motion of the limb) and the patterns of ICMS required to evoke S1 activation that mimics that of natural inputs. These maps will account for both the dynamics of neural responses and the biophysics of ICMS. We anticipate that this biomimetic approach will evoke intuitive sensations that require little or no training to interpret. We will validate the maps by comparing natural and ICMS-evoked S1 activity using novel hardware that allows for concurrent ICMS and neural recording. In Aim 2, we will test the ability of monkeys to recognize objects using artificial touch. Having learned to identify real objects by touch, animals will explore virtual objects with an avatar