Architectural Exploration of Neuromorphic Compression based Neural Sensing for Next-Gen Wireless implantable-BMI

This work explores the architectural trade-offs and implications of a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wireless iBMI. We use quantitative metrics such as root-mean-square error and correlation coefficient between the original and recovered signal to assess the effect of neuromorphic compression on spike shape, and spike detection accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks. We demonstrate that a data compression ratio of > 50 can be achieved by selective transmission of event pulses generated in different modes for large electrode arrays with a correlation coefficient of approximate to 0.9 and a spike detection accuracy of over 90%.

Automated summary

This study explores a neural sensing architecture with neuromorphic compression and an address-event representation inspired readout protocol for next-gen, massively parallel wireless iBMI. The effects of neuromorphic compression on spike shape, spike detection accuracy, sensitivity, and false detection rate are assessed using quantitative metrics such as root-mean-square error and correlation coefficient between the original and recovered signal, to understand the impact of compression on downstream iBMI tasks. The results demonstrate that a data compression ratio of > 50 can be achieved by selectively transmitting event pulses generated in different modes, with a correlation coefficient of approximately 0.9 and a spike detection accuracy of over 90%.