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Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Isha Gupta,A. Serb,A. Khiat,R. Zeitler,S. Vassanelli,T. Prodromakis

Published 2016 in Nature Communications

ABSTRACT

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces. The need for intelligent compression of big data, for example in neuroscience, has sparked interest in neuromorphic data processing. Here, Gupta et al.use memristors as event integrators to encode and compress neuronal spiking activity recorded by multi-electrode arrays.

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