A high-performance asynchronous readout circuit with cascade function for a neural recording micro-electrode array

The data-driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro-electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large-scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large-scale MEA customization through cascading, and reduce the bandwidth of large-scale MEA through spike compression. Firstly, based on in-pixel spike detection, a row-based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large-scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation-based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large-scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.