Dynamic Time-Multiplexed Switching in a PCB-Based Neural Recording System

Abstract

High-density microelectrode arrays make it possible to obtain detailed recordings of neural population activity. However, they are fundamentally limited by the number of available readout channels in a given system. Switch-matrices attempt to address this issue by flexibly routing many electrodes to a smaller set of amplifiers, but existing implementations are limited to static configurations during each recording. Consequently, continuous temporal coverage is restricted to a subset of electrodes. In this project, I am investigating whether dynamic time-multiplexed switching can be adapted to a PCB-based recording system. I have designed a dynamically reconfigurable switch matrix that routes 480 electrodes, arranged in a 24 x 20 array, to 120 headstage inputs by rapidly cycling though 4 electrode groups of 120 each. The feasibility of routing is validated using a min-cost/max-flow algorithm to generate the best switch configurations. A microcontroller-based control system sends configuration bitstreams to MAX14803 chips distributed throughout the system and applies atomic switching updates while maintaining settling delays to mitigate switching artifacts. I will analyze the constraints surrounding the switching speed, signal integrity, and noise as well as the trade-offs of working with discrete analog switches on a PCB. This paper aims to show that switch matrices based on PCBs can provide sufficient flexibility and timing performance for electrophysiological exploration, creating a cost-effective platform for testing time-multiplexed neural recording architectures.