Current retinal prostheses provide electrical stimulation without feedback from the stimulated neurons. Incorporation of multichannel recording electronics would typically require trans-scleral cables for power supply and data transmission. In this work, we explore a wireless, optoelectronic, miniature, modular, and distributed electro-neural interface for recording, which we call Sensory Particles with Optical Telemetry (SPOT). It can be used in an advanced, bi-directional retinal prosthesis and other sensory applications. Emphasis is placed on the novel telemetry stage. SPOTs are powered by near-infrared light and transmit information by light. As a proof of concept, we designed and built a low-power, small-footprint linear transconductance circuit utilizing chopper stabilization in 130nm CMOS. Our design achieved 57 mS transconductance within 3.5 kHz bandwidth, and a near-infrared (NIR) power density of 0.5 mW/mm², well within the ocular and thermal safety limits. The telemetry circuit consumes 0.015 mm² area, and each SPOT can be powered by a single photovoltaic (PV) supply of area 0.0056 mm². Electrical spikes transmitted by an 850nm LED were detected with 15 dB SNR, at the output of the optical link.

This paper describes an architecture for the massively parallel digitization of neural action potentials. The scheme achieves simultaneous data compression and channel multiplexing through wired-OR interactions within an array of single-slope A/D converters. The achieved compression is lossy but effective at retaining the critical samples belonging to action potential spikes. Simulation results using ex-vivo experimental data from a 512-channel array show compression rates up to ∼73x while maintaining ≥90% reconstruction coverage for parasol cells in the primate retina.

Neural interfaces of the future will be used to help restore lost sensory, motor, and other capabilities. However, realizing this futuristic promise requires a major leap forward in how electronic devices interface with the nervous system. Next generation neural interfaces must support parallel recording from tens of thousands of electrodes within the form factor and power budget of a fully implanted device, posing a number of significant engineering challenges. In this paper, we exploit sparsity and diversity of neural signals to achieve simultaneous data compression and channel multiplexing for neural recordings. The architecture uses wired-OR interactions within an array of single-slope A/D converters to obtain massively parallel digitization of neural action potentials. The achieved compression is lossy but effective at retaining the critical samples belonging to action potentials, enabling efficient spike sorting and cell type identification. Simulation results of the architecture using data obtained from primate retina ex-vivo with a 512-channel electrode array show average compression rates up to  ∼ 40× while missing less than 5% of cells. In principle, the techniques presented here could be used to design interfaces to other parts of the nervous system.