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.

TiO₂ is commonly used as the active switching layer in resistive random access memory. The electrical characteristics of these devices are directly related to the fundamental conditions inside the TiO₂ layer and at the interfaces between it and the surrounding electrodes. However, it is complex to disentangle the effects of film “bulk” properties and interface phenomena. The present work uses hard X‐ray photoemission spectroscopy (HAXPES) at different excitation energies to distinguish between these regimes. Changes are found to affect the entire thin film, but the most dramatic effects are confined to an interface. These changes are connected to oxygen ions moving and redistributing within the film. Based on the HAXPES results, post‐deposition annealing of the TiO₂ thin film was investigated as an optimisation pathway in order to reach an ideal compromise between device resistivity and lifetime. The structural and chemical changes upon annealing are investigated using X‐ray absorption spectroscopy and are further supported by a range of bulk and surface sensitive characterisation methods. In summary, it is shown that the management of oxygen content and interface quality is intrinsically important to device behavior and that careful annealing procedures are a powerful device optimisation technique.

Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial.

Neuromodulation of the immune system has been proposed as a novel therapeutic strategy for the treatment of inflammatory conditions. We recently demonstrated that stimulation of near-organ autonomic nerves to the spleen can be harnessed to modulate the inflammatory response in an anesthetized pig model. The development of neuromodulation therapy for the clinic requires chronic efficacy and safety testing in a large animal model. This manuscript describes the effects of longitudinal conscious splenic nerve neuromodulation in chronically-implanted pigs. Firstly, clinically-relevant stimulation parameters were refined to efficiently activate the splenic nerve while reducing changes in cardiovascular parameters. Subsequently, pigs were implanted with a circumferential cuff electrode around the splenic neurovascular bundle connected to an implantable pulse generator, using a minimally-invasive laparoscopic procedure. Tolerability of stimulation was demonstrated in freely-behaving pigs using the refined stimulation parameters. Longitudinal stimulation significantly reduced circulating tumor necrosis factor alpha levels induced by systemic endotoxemia. This effect was accompanied by reduced peripheral monocytopenia as well as a lower systemic accumulation of CD16⁺CD14^highpro-inflammatory monocytes. Further, lipid mediator profiling analysis demonstrated an increased concentration of specialized pro-resolving mediators in peripheral plasma of stimulated animals, with a concomitant reduction of pro-inflammatory eicosanoids including prostaglandins. Terminal electrophysiological and physiological measurements and histopathological assessment demonstrated integrity of the splenic nerves up to 70 days post implantation. These chronic translational experiments demonstrate that daily splenic nerve neuromodulation,viaimplanted electronics and clinically-relevant stimulation parameters, is well tolerated and is able to prime the immune system toward a less inflammatory, pro-resolving phenotype.

Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this brief, we present an algorithm for identifying switchable devices, i.e., devices that can be programmed in distinct resistive states and that change their state predictably and repeatedly in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardized bias protocols. The test routine is applied on distinct elements of 32×32 resistive-random-access-memory (RRAM) crossbar arrays and yields a measure of device switchability in the form of a statistical significance p-value. Ranking devices by p-value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from nonfunctional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array, as well as yield metrics.

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.