An analytical approach to engineer multistability in the oscillatory response of a pulse-driven ReRAM.

A nonlinear system, exhibiting a unique asymptotic behaviour, while being continuously subject to a stimulus from a certain class, is said to suffer from fading memory. This interesting phenomenon was first uncovered in a non-volatile tantalum oxide-based memristor from Hewlett Packard Labs back in 2016 out of a deep numerical investigation of a predictive mathematical description, known as the Strachan model, later corroborated by experimental validation. It was then found out that fading memory is ubiquitous in non-volatile resistance switching memories. A nonlinear system may however also exhibit a local form of fading memory, in case, under an excitation from a given family, it may approach one of a number of distinct attractors, depending upon the initial condition. A recent bifurcation study of the Strachan model revealed how, under specific train stimuli, composed of two square pulses of opposite polarity per cycle, the simplest form of local fading memory affects the transient dynamics of the aforementioned Resistive Random Access Memory cell, which, would asymptotically act as a bistable oscillator. In this manuscript we propose an analytical methodology, based on the application of analysis tools from Nonlinear System Theory to the Strachan model, to craft the properties of a generalised pulse train stimulus in such a way to induce the emergence of complex local fading memory effects in the nano-device, which would consequently display an interesting tuneable multistable oscillatory response, around desired resistance states. The last part of the manuscript discusses a case study, shedding light on a potential application of the local history erase effects, induced in the device via pulse train stimulation, for compensating the unwanted yet unavoidable drifts in its resistance state under power off conditions.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

Infrared Thermographic Imaging of Chest Wall Perfusion in Patients Undergoing Coronary Artery Bypass Grafting.

Coronary artery disease represents a leading cause of death worldwide, to which the coronary artery bypass graft (CABG) is the main method of treatment in advanced multiple vessel disease. The use of the internal mammary artery (IMA) as a graft insures an improved long-term survival, but impairment of chest wall perfusion often leads to surgical site infection and increased morbidity and mortality. Infrared thermography (IRT) has established itself in the past decades as a non-invasive diagnostic technique. The applications vary from veterinary to human medicine and from head to toe. In this study we used IRT in 42 patients receiving CABG to determine the changes in skin surface temperature preoperatively, two hours, 24 h and 6 days after surgery. The results showed a significant and independent drop of surface temperature 2 h after surgery on the whole surface of the chest wall, as well as a further reduction on the left side after harvesting the IMA. The temperature returned to normal after 24 h and remained so after 6 days. The study has shown that IRT is sufficiently sensitive to demonstrate the known, subtle reduction in chest wall perfusion associated with IMA harvesting.

Registration of IRT and visible light images in neurosurgery: analysis and comparison of automatic intensity-based registration approaches.

PURPOSE: The purpose of this study is to analyze and compare six automatic intensity-based registration methods for intraoperative infrared thermography (IRT) and visible light imaging (VIS/RGB). The practical requirement is to get a good performance of Euclidean distance between manually set landmarks in reference and target images as well as to achieve a high structural similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR) with respect to the reference image. METHODS: In this study, preprocessing is applied to bring both image types to a similar intensity. Similarity transformation is employed to align roughly IRT and visible light images. Two optimizers and two measures are used in this process. Thereafter, due to locally different displacement of the brain surface through respiration and heartbeat, two non-rigid transformations are applied, and finally, a bicubic interpolation is carried out to compensate for the resulting estimated transformation. Performance was assessed using eleven image datasets. The registration accuracy of the different computational approaches was assessed based on SSIM and PSNR. Additionally, five concise landmarks for each dataset were selected manually in reference and target images and the Euclidean distance between the corresponding landmarks was compared. RESULTS: The results are showing that the combination of normalized intensity, mutual information measure with one-plus-one evolutionary optimizer in combination with Demon registration results in improved accuracy and performance as compared to all other methods tested here. Furthermore, the obtained results led to [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] registrations for datasets 1, 2, 5, 7, and 8 with respect to the second best result by calculating the mean Euclidean distance of five landmarks. CONCLUSIONS: We conclude that the mutual information measure with one-plus-one evolutionary optimizer in combination with Demon registration can achieve better accuracy and performance to those other methods mentioned here for automatic registration of IRT and visible light images in neurosurgery.

Coherent false seizure prediction in epilepsy, coincidence or providence?

OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. METHODS: We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. RESULTS: For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. CONCLUSIONS: Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. SIGNIFICANCE: The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.

Selective and self-validating breath-level detection of hydrogen sulfide in humid air by gold nanoparticle-functionalized nanotube arrays.

We demonstrate the selective detection of hydrogen sulfide at breath concentration levels under humid airflow, using a self-validating 64-channel sensor array based on semiconducting single-walled carbon nanotubes (sc-SWCNTs). The reproducible sensor fabrication process is based on a multiplexed and controlled dielectrophoretic deposition of sc-SWCNTs. The sensing area is functionalized with gold nanoparticles to address the detection at room temperature by exploiting the affinity between gold and sulfur atoms of the gas. Sensing devices functionalized with an optimized distribution of nanoparticles show a sensitivity of 0.122%/part per billion (ppb) and a calculated limit of detection (LOD) of 3 ppb. Beyond the self-validation, our sensors show increased stability and higher response levels compared to some commercially available electrochemical sensors. The cross-sensitivity to breath gases NH3 and NO is addressed demonstrating the high selectivity to H2S. Finally, mathematical models of sensors' electrical characteristics and sensing responses are developed to enhance the differentiation capabilities of the platform to be used in breath analysis applications. Electronic Supplementary Material: Supplementary material (details on the dielectrophoretic deposition, AuNP functionalization optimization, full range of experimental and model H2S sensing response up to 820 ppb, and sensing response to NO gas) is available in the online version of this article at 10.1007/s12274-021-3771-7.

On Local Activity and Edge of Chaos in a NaMLab Memristor.

Local activity is the capability of a system to amplify infinitesimal fluctuations in energy. Complex phenomena, including the generation of action potentials in neuronal axon membranes, may never emerge in an open system unless some of its constitutive elements operate in a locally active regime. As a result, the recent discovery of solid-state volatile memory devices, which, biased through appropriate DC sources, may enter a local activity domain, and, most importantly, the associated stable yet excitable sub-domain, referred to as edge of chaos, which is where the seed of complexity is actually planted, is of great appeal to the neuromorphic engineering community. This paper applies fundamentals from the theory of local activity to an accurate model of a niobium oxide volatile resistance switching memory to derive the conditions necessary to bias the device in the local activity regime. This allows to partition the entire design parameter space into three domains, where the threshold switch is locally passive (LP), locally active but unstable, and both locally active and stable, respectively. The final part of the article is devoted to point out the extent by which the response of the volatile memristor to quasi-static excitations may differ from its dynamics under DC stress. Reporting experimental measurements, which validate the theoretical predictions, this work clearly demonstrates how invaluable is non-linear system theory for the acquirement of a comprehensive picture of the dynamics of highly non-linear devices, which is an essential prerequisite for a conscious and systematic approach to the design of robust neuromorphic electronics. Given that, as recently proved, the potassium and sodium ion channels in biological axon membranes are locally active memristors, the physical realization of novel artificial neural networks, capable to reproduce the functionalities of the human brain more closely than state-of-the-art purely CMOS hardware architectures, should not leave aside the adoption of resistance switching memories, which, under the appropriate provision of energy, are capable to amplify the small signal, such as the niobium dioxide micro-scale device from NaMLab, chosen as object of theoretical and experimental study in this work.

Motion Correction in Multimodal Intraoperative Imaging.

Thermographic imaging accompanied with time-resolved analysis is a promising technique for intraoperative imaging in neurosurgery. However, motion due to breathing and pulse of the patient introduces large inaccuracies to the demarcation of normal and pathological brain tissue. Since movements and physiological processes are both manifested as temperature variations, we employ co-registered visual-light images to unambiguously detect motion. In this article, we propose a feature-based approach which is selected from four best-known methods after thorough performance comparison. Complementing our previous work, we evaluate the performance of our methods by applying a frequency analysis and similarity measurements. Our approach enables an accurate motion correction without affecting physiological temperature shifts. Furthermore, real-time performance of the implementation is enabled by serial acceleration and parallelization methods.

Intraoperative motion correction in neurosurgery: a comparison of intensity- and feature-based methods.

The intraoperative identification of normal and anomalous brain tissue can be disturbed by pulsatile brain motion and movements of the patient and surgery devices. The performance of four motion correction methods are compared in this paper: Two intensity-based, applying optical flow algorithms, and two feature-based, which take corner features into account to track brain motion. The target registration error with manually selected marking points and the temporal standard deviation of intensity were analyzed in the evaluation. The results reveal the potential of the two types of methods.

Registration and Fusion of Thermographic and Visual-Light Images in Neurosurgery.

An intraoperative imaging system facilitating the localisation and characterisation of functional areas, pathological tissue, or perfusion disorders, could enormously support medical decisions during neurosurgical interventions and, thus, reduce the risk for the patients. To provide both structural and functional information of the brain tissue to the surgeon, a novel multimodal approach based on the measurement of long-wave infrared radiation and visual-light imaging is very promising. In this contribution, we discuss various methods for the registration and fusion of thermographic and visual-light images. The methods are evaluated quantitatively and qualitatively regarding their practicability during surgery. Furthermore, we introduce appropriate architectures for a digital hardware implementation of the registration and fusion algorithms. The designs are implemented on our reconfigurable intraoperative imaging system, revealing real-time processing performance.

Neuronal synapse as a memristor: modeling pair- and triplet-based STDP rule.

We propose a new memristive model for the neuronal synapse based on the spike-timing-dependent plasticity (STDP) protocol, considering both long-term and short-term plasticity in the synapse. Higher-order behavior is modeled by a memristor with adaptive thresholds, which realizes the well-established suppression principle of Froemke. We assume a mechanism of variable thresholds adapting to synaptic potentiation (LTP) and depression (LTD), which reproduces the refractory time in the weight modification. The corresponding dynamical process is governed by a set of ordinary differential equations. Interestingly, the Froemke's model and our memristive model, based on two completely different mechanisms, are found to be quantitatively equivalent for the 'pre-post-pre' case and 'post-pre-post' case. A relation of the adaptive thresholds to short-term plasticity is addressed.

Adaptive Neuromorphic Architecture (ANA).

We designed Adaptive Neuromorphic Architecture (ANA) that self-adjusts its inherent parameters (for instance, the resonant frequency) naturally following the stimuli frequency. Such an architecture is required for brain-like engineered systems because some parameters of the stimuli (for instance, the stimuli frequency) are not known in advance. Such adaptivity comes from a circuit element with memory, namely mem-inductor or mem-capacitor (memristor's sisters), which is history-dependent in its behavior. As a hardware model of biological systems, ANA can be used to adaptively reproduce the observed biological phenomena in amoebae.

Analysis of multidimensional neural activity via CNN-UM.

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.