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

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

Detection of voltage fluctuations in the adhesion region of cells with field effect transistors

Durch die Verwendung von rauscharmen Elektrolyt-Oxid-Silizium-Feldeffekttransistoren auf einem CMOS-Chip konnten Spannungsfluktuationen im Spalt zwischen Zellmembran und Chipoberfläche detektiert werden. Die theoretische Betrachtung der beitragenden Rauschprozesse berücksichtigte die Einflüsse von Schichtwiderstand, Ionenkanälen und der Bindung von Protonen an die Oxidoberfläche. Im Experiment wurde gezeigt, dass die Bindung von Protonen einen nicht zu vernachlässigenden Beitrag zum Rauschen liefert. Bei den Zelllinien HEK293 und MDCK-II und bei Schneckenneuronen konnte der Schichtwiderstand aus den Rauschdaten ortsaufgelöst bestimmt werden. An einer Kaninchenretina war es möglich aus dem Rauschen auf die für Neurointerfacing wichtige Güte des Kontaktes zwischen Gewebe und Chip zu schließen.Using low-noise electrolyte-oxide-silicon-field effect transistors on a CMOS-chip we were able to detect voltage fluctuations in the cleft formed by cell membrane and chip surface. Theoretical treatment of the contributing noise processes included the influence of the sheet resistance, ion channels and binding of protons to the oxide surface. It was experimentally shown that proton binding makes a significant noise contribution. We could extract spatial maps of the sheet resistance from noise data of cell lines HEK293 and MDCK-II and snail neurons. For rabbit retina we determined the quality of the chip-tissue contact, which is important for neuronal interfacing applications