The advent of advanced neuronal interfaces offers great promise for linking brain functions to electronics. A major bottleneck in achieving this is real-time processing of big data that imposes excessive requirements on bandwidth, energy and computation capacity; limiting the overall number of bio-electronic links. Here, we present a novel monitoring system concept that exploits the intrinsic properties of memristors for processing neural information in real time. We demonstrate that the inherent voltage thresholds of solid-state TiOx memristors can be useful for discriminating significant neural activity, i.e. spiking events, from noise. When compared with a multi-dimensional, principal component feature space threshold detector, our system is capable of recording the majority of significant events, without resorting to computationally heavy off-line processing. We also show a memristive integrating sensing array that discriminates neuronal activity recorded in-vitro. We prove that information on spiking event amplitude is simultaneously transduced and stored as non-volatile resistive state transitions, allowing for more efficient data compression, demonstrating the memristors' potential for building scalable, yet energy efficient on-node processors for big data.
@article{arxiv.1507.06832,
title = {Memristive integrative sensors for neuronal activity},
author = {Isha Gupta and Alexantrou Serb and Ali Khiat and Ralf Zeitler and Stefano Vassanelli and Themistoklis Prodromakis},
journal= {arXiv preprint arXiv:1507.06832},
year = {2015}
}