Prosthesis Control Using Spike Rate Coding in the Retina Photoreceptor Cells

Abstract

Volitional control of prostheses is most commonly achieved by myoelectric signalling. The electromyograph (EMG) is detected and processed by a controller, that decodes and relates the signal to the corresponding position of the prosthetic. Myoelectric signalling is limited in users by two factors: lack of nerve endings corresponding to the position of the amputation, and neurological damage resulting in poor signal control. Improved prosthesis control has been demonstrated by the addition of feedback sensors based on computer vision and inertial measurement units. Computer vision requires a significant level of processing, resulting in a high latency and high power usage. In this paper, we propose a means of overcoming this limitation by use of in-vivo retinal signalling to complement EMG for improved control. This is demonstrated using a real-time conductancebased simulator as the sole method of control for an upper-limb prosthesis. Input image streams are received by a camera and used to activate the combined rod and cone photoreceptor cell responses. This in turn generates a spike train which is counted and averaged over time, and passed to an Arduino-based control system which modulates the behavior of the prosthesis. We seek to use this system to lower the experimental barriers of in-vivo ganglion electrical signalling by presenting a way to use retina emulation. A link to the simulator is provided.

Training Spiking Neural Networks Using Lessons From Deep Learning

“…To handle such widely varying stimuli, sensory transduction systems likely compress stimulus intensity with a logarithmic dependency. For this reason, a logarithmic relation between spike times and input feature intensity is ubiquitous in the literature (see Appendix B2) [101], [102].…”