Detection of electrical neural activity has been possible for almost a century. Despite the progress in the last century, neural recording technologies remain fundamentally constrained in their scale by the power they are able to dissipate. As channel counts have increased, and electrodes have grown flexible and microscale, connectorization to neural recording circuits has become ever more challenging. All-optical neural recording technologies hold the promise to eliminate these barriers. However, until this point, it has been impossible to quantitatively compare existing electrical recording technology with emerging optical solutions. I introduce a novel figure of merit EQ, which overcomes this challenge, and allows direct and quantitative comparison between arbitrary technologies. I propose a novel neural recording architecture which can achieve a lower EQ than existing electronic methods, consisting of free-floating passive optical voltage sensors. These sensors have no active circuitry and require no stored power, and can be interrogated wirelessly at will. I describe progress towards the realization of these sensors, including their design, fabrication, and characterization, as well as their use for digital communication through highly scattering tissue. Additionally, I show that the same sensors and the same framework can be applied to solve some outstanding problems in these sensors use in microgrid applications for kV-range sensing.
May 1, 2023
Jordan Edmunds EECS Department University of California, Berkeley Technical Report No. UCB/EECS-2023-55 May 1, 2023