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

Isha Gupta (), Alexantrou Serb, Ali Khiat, Ralf Zeitler, Stefano Vassanelli and Themistoklis Prodromakis
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Isha Gupta: Faculty of Physical Science and Engineering, University of Southampton
Alexantrou Serb: Faculty of Physical Science and Engineering, University of Southampton
Ali Khiat: Faculty of Physical Science and Engineering, University of Southampton
Ralf Zeitler: Max Planck Institute for Intelligent Systems
Stefano Vassanelli: University of Padova
Themistoklis Prodromakis: Faculty of Physical Science and Engineering, University of Southampton

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: 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.

Date: 2016
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DOI: 10.1038/ncomms12805

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