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• W2512274313 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." @default.
• W2512274313 created "2016-09-16" @default.
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• W2512274313 date "2016-09-26" @default.
• W2512274313 modified "2023-10-14" @default.
• W2512274313 title "Real-time encoding and compression of neuronal spikes by metal-oxide memristors" @default.
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• W2512274313 doi "https://doi.org/10.1038/ncomms12805" @default.
• W2512274313 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5052668" @default.
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• W2512274313 hasPublicationYear "2016" @default.
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