Decoding sensory feedback from firing rates of afferent ensembles recorded in cat dorsal root ganglia in normal locomotion

Abstract

Sensory feedback is required by biological motor control systems to maintain stability, respond to perturbations, and adapt. Similarly, motor neuroprostheses require feedback to provide natural and complete restoration of motor functions. In this paper, we show that ensemble firing rates from the body's mechanoreceptors can provide a natural source of kinematic state feedback and could be useful for prosthetic control. Single unit recordings from multiple primary afferent neurons were obtained during walking using multichannel electrode arrays implanted chronically in the L7 dorsal root ganglia of three cats. We typically recorded simultaneously from over 20-30 neurons during the first 7-14 days after surgery, but recordings gradually worsened thereafter. Histology indicates that a ring of inflammatory and connective tissues (100 microm thick) develops around each microelectrode and likely contributes to the degradation in recording quality. Accurate estimates of the hindlimb trajectory were made using a linear filter with inputs from only a few neurons highly correlated with limb kinematics. The coefficients for the linear filter were identified in a least-squares fit with 5-10 s of walking data (model training stage). The estimated and actual trajectories of separate walking data generally match well for walking at a range of speeds accounting for 63 +/- 22% (mean +/- S.D. for hip, knee, and ankle) of the variance in joint angle and 72 +/- 4% of the variance in joint angular velocities. These results indicate that a neural interface with primary sensory neurons in the dorsal root ganglion can provide accurate kinematic state information that may be useful for closed loop control of a neuroprosthesis.