Neuromorphic sensor front-end

A neuromorphic front-end converts the analog neural signal into events at the sensor, before any conventional digitisation, so that downstream compute is already event-driven. Common schemes are level-crossing (send an event only when the signal crosses a threshold) and delta modulation (encode changes), producing an asynchronous, activity-dependent event stream — the neural analogue of an event camera's per-pixel change detection. This removes the constant-rate ADC and its steady power draw and naturally compresses quiet epochs.

The design must handle amplifier noise, baseline drift, and artifacts that generate spurious events, and must set thresholds that survive nonstationary signals. Well-designed spiking front-ends can cut data volume and power substantially, which is attractive for high-channel implants, but reconstruction fidelity and robustness to motion/EMG artifacts remain active problems.