Abstract—This paper investigates the performance of commonly used spike detection algorithms (Absolute Amplitude Thresholding and Non-linear Energy Operator) on compressed neural signals using a novel wired-OR lossy compression algorithm. Performing compression with the wired-OR architecture mainly removes the noisy baseline and preserves spikes in the neural signal. As a result, the spike detection sensitivity and accuracy improve or stay similar after compression. In addition, this paper proposes a new spike detection algorithm, the non-zero spike detector, that can be efficiently integrated into hardware with the wired-OR compression scheme. By using a firing rate-based approach to optimize the threshold in the non-zero spike detector, the proposed technique outperforms both Absolute Amplitude Thresholding and the Non-linear Energy Operator across different signal-to-noise ratios and firing rates. The wired-OR readout architecture, in combination with the non-zero spike detector, is a promising approach to achieve massive compression while preserving the neural signal and maintaining spike detection performance.