Ultra-fine signal classification using memristor-enabled hardware

Abstract

Neural activity recording system promotes the development of diagnostic and therapeutic programs and neuroscience research. Direct recordings of neural signals from the brain have helped scientists access to study and unlock the secrets of neural coding gradually. This can be realised by applying implantable neural recording systems to monitor and record neural signals. Then, the neural information can be transmitted to the external device for processing, storage or application. However, the power consumption of the neural recording system is the primary constraint to monitoring large groups of neurons. It leads the development of neural recording systems in two directions: 'high-channel-count but wired' and 'wireless but low-channel-count'. To address the power issue, we proposed a neural front-end that aims to detect neural spikes by thresholding and output as one-bit digital data so that the afterwards processing can only work on spikes rather than processing all the data points. The most significant feature is that we induce memristors as trimming devices to tune the threshold voltage for spike detection.
Meanwhile, it contributes to rejecting up to 50mV DC offset from electrodes. The measurement presents that the memristor-based pre-amplifier is capable of achieving above 95% spike detection accuracy with hundreds of nanowatt power consumption per channel. This design indicates a promising approach to conduct spike-detection on-chip with low power consumption and demonstrates the potential of a hybrid memristor/CMOS circuit for power-efficient large-scale neural interfacing application.

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