Equilibrium Propagation for Memristor-Based Recurrent Neural Networks

Among the recent innovative technologies, memristor (memory-resistor) has attracted researchers attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule that is gaining particular interest because of its simplicity and biological plausibility. The overall goal of this work is to provide a novel (theoretical) analog computing platform based on memristor devices and recurrent neural networks that exploits the memristor device physics to implement two variations of the backpropagation algorithm: recurrent backpropagation and equilibrium propagation. In the first learning technique, the use of memristor–based synaptic weights permits to propagate the error signals in the network by means of the nonlinear dynamics via an analog side network. This makes the processing non-digital and different from the current procedures. However, the necessity of a side analog network for the propagation of error derivatives makes this technique still highly biologically implausible. In order to solve this limitation, it is therefore proposed an alternative solution to the use of a side network by introducing a learning technique used for energy-based models: equilibrium propagation. Experimental results show that both approaches significantly outperform conventional architectures used for pattern reconstruction. Furthermore, due to the high suitability for VLSI implementation of the equilibrium propagation learning rule, additional results on the classification of the MNIST dataset are here reported

A dynamic system approach to spiking second order memristor networks

Second order memristors are two terminal devices that present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromor- phic computing. In particular, memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems by exploiting temporal learning techniques. On the other hand, neuromorphic studies and experiments have revealed the existence of differ- ent kinds of plasticity and have shown the effect of calcium concentration on synaptic changes. Computational studies have investigated the behavior of spiking networks in the context of supervised, unsupervised, and reinforcement learning. In this paper, we first derive a simplified, almost analytical, model of a second-order memristor, only involving two variables, the mem- conductance, and the temperature, directly attributable to the synaptic efficacy and to the calcium concentration. Then we study in detail the response of a single memristive synapse to the most relevant plasticity models, including cycles of spike pairs, triplets, and quadruplets at different frequencies. Finally, we accurately characterize memristor spiking networks as discrete nonlinear dynamic systems, with mem-conductances as state variables and pre and postsynaptic spikes as inputs and outputs, respectively. The result shows that the model developed in this manuscript can explain and accurately reproduce a significant portion of observed synaptic behaviors, including those not captured by classical spike pair-based STDP models. Furthermore, under such an approach, the global dynamic behavior of memristor networks and the related learning mechanisms can be deeply analyzed by employing advanced nonlinear dynamic techniques

Equilibrium Propagation and (Memristor-based) Oscillatory Neural Networks

Weakly Connected Oscillatory Networks (WCONs) are bio-inspired models which exhibit associative memory properties and can be exploited for information processing. It has been shown that the nonlinear dynamics of WCONs can be reduced to equations for the phase variable if oscillators admit stable limit cycles with nearly identical periods. Moreover, if connections are symmetric, the phase deviation equation admits a gradient formulation establishing a one-to-one correspondence between phase equilibria, limit cycle of the WCON and minima of the system’s potential function. The overall objective of this work is to provide a simulated WCON based on memristive connections and Van der Pol oscillators that exploits the device mem-conductance programmability to implement a novel local supervised learning algorithm for gradient models: Equilibrium Propagation (EP). Simulations of the phase dynamics of the WCON system trained with EP show that the retrieval accuracy of the proposed novel design outperforms the current state-of-the-art performance obtained with the Hebbian learning

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI

Spectral Ranking in Complex Networks Using Memristor Crossbars

Various centrality measures have been proposed to identify the influence of each node in a complex network. Among the most popular ranking metrics, spectral measures stand out from the crowd. They rely on the computation of the dominant eigenvector of suitable matrices related to the graph: EigenCentrality, PageRank, Hyperlink Induced Topic Search (HITS) and Stochastic Approach for Link-Structure Analysis (SALSA). The simplest algorithm used to solve this linear algebra computation is the Power Method. It consists of multiple Matrix-Vector Multiplications (MVMs) and a normalization step to avoid divergent behaviours. In this work, we present an analog circuit used to accelerate the Power Iteration algorithm including current-mode termination for the memristor crossbars and a normalization circuit. The normalization step together with the feedback loop of the complete circuit ensure stability and convergence of the dominant eigenvector. We implement a transistor level peripheral circuitry around the memristor crossbar and take non-idealities such as wire parasitics, source driver resistance and finite memristor precision into account. We compute the different spectral centralities to demonstrate the performance of the system. We compare our results to the ones coming from the conventional digital computers and observe significant energy savings while maintaining a competitive accuracy

Acetylcholine Use in Modern Cardiac Catheterization Laboratories: A Systematic Review

Background: The use of acetylcholine for the diagnosis of vasospastic angina is recommended by international guidelines. However, its intracoronary use is still off-label due to the absence of safety studies. We aimed to perform a systematic review of the literature to identify adverse events related to the intracoronary administration of acetylcholine for vasoreactivity testing to fill this gap. Methods and results: We conducted a systematic review of observational studies and randomized controlled trials dealing with the intracoronary administration of acetylcholine. Articles were searched in MEDLINE (PubMed) using the MeSH strategy. Three independent reviewers determined whether the studies met the inclusion and exclusion criteria. A total of 434 articles were selected. Data concerning clinical characteristics, study population, acetylcholine dosage, and adverse effects were retrieved from the articles. Overall, 71,566 patients were included, of which only 382 (0.5%) developed one adverse event, and there were no fatal events reported (0%). Conclusions: Intracoronary administration of acetylcholine in the setting of coronary spasm provocation testing is safe and plays a central role in the evaluation of coronary vasomotion disorders, making it worthy of becoming a part of clinical practice in all cardiac catheterization laboratories

AI technology for remote clinical assessment and monitoring

Objective: To report the clinical validation of an innovative, artificial intelligence (AI)-powered, portable and non-invasive medical device called Wound Viewer. The AI medical device uses dedicated sensors and AI algorithms to remotely collect objective and precise clinical data, including three-dimensional (3D) wound measurements, tissue composition and wound classification through the internationally recognised Wound Bed Preparation (WBP) protocol; this data can then be shared through a secure General Data Protection Regulation (GDPR)- and Health Insurance Portability and Accountability Act (HIPAA)-compliant data transfer system. This trial aims to test the reliability and precision of the AI medical device and its ability to aid health professionals in clinically evaluating wounds as efficiently remotely as at the bedside. Method: This non-randomised comparative clinical trial was conducted in the Clinica San Luca (Turin, Italy). Patients were divided into three groups: (i) patients with venous and arterial ulcers in the lower limbs; (ii) patients with diabetes and presenting with diabetic foot syndrome; and (iii) patients with pressure ulcers. Each wound was evaluated for area, depth, volume and WBP wound classification. Each patient was examined once and the results, analysed by the AI medical device, were compared against data obtained following visual evaluation by the physician and research team. The area and depth were compared with a Kruskal–Wallis one-way analysis of variations in the obtained distribution (expected p-value>0.1 for both tests). The WBP classification and tissue segmentation were analysed by directly comparing the classification obtained by the AI medical device against that of the testing physician. Results: A total of 150 patients took part in the trial. The results demonstrated that the AI medical device's AI algorithm could acquire objective clinical parameters in a completely automated manner. The AI medical device reached 97% accuracy against the WBP classification and tissue segmentation analysis compared with that performed in person by the physician. Moreover, data regarding the measurements of the wounds, as analysed through the Kruskal–Wallis technique, showed that the data distribution proved comparable with the other methods of measurement previously clinically validated in the literature (p=0.9). Conclusion: These findings indicate that remote wound assessment undertaken by physicians is as effective through the AI medical device as bedside examination, and that the device was able to assess wounds and provide a precise WBP wound classification. Furthermore, there was no need for manual data entry, thereby reducing the risk of human error while preserving high-quality clinical diagnostic data

Effects of habitat fragmentation of a mediterranean marine reef on the associated fish community : insights from biological traits analysis

Habitat fragmentation (HF) is an ecological process, which is potentially also one of the main causes of diversity loss. Many studies have debated the best tools to adopt for assessing the effects of HF. The traditional application of biodiversity metrics might not fully describe the biotic community associated to a particular habitat or the ongoing ecological processes. The community-weighted mean (CWM) seems to be a valid investigation index, since biological traits (BTs) of the associated community are selected by local environmental factors. Furthermore, by combining species with common BTs into Functional Groups (FGs), it is possible to account for ecological functions that are supported by the inclusion of the response of key species within the same context. In our case study, we investigated the possible effect of HF of different Sicilian vermetid reefs on the associated infralittoral fish community based on the (i) vermetid fragmentation level, (ii) nature of the infralittoral substratum and (iii) conservational level of protection. We expected HF to be the main factor in shaping the local fish community; however, the nature of the infralittoral substratum proved to be the principal driver of the ichthyofaunal community. By analysing separately the two infralittoral substrata considered in the study, we observed how HF might affect the associated fish community differently. A pristine vermetid reef seems to sustain a higher number of FGs when established on a rocky substratum. On the other hand, in the presence of a sandy substratum, a fragmented vermetid reef seems to attract a more functionally rich fish community than those accounted for a pristine status. Our results provide some evidence in support of the need to include a broad spectrum of community function descriptors for a more comprehensive characterisation of a habitat and for the assessment of the functioning of its ecosystem.peer-reviewe

Bridging the knowledge gap on the distribution and typology of vermetid bioconstructions along the Maltese coastline : an updated assessment

In the Maltese Islands, insufficient attention has been paid to vermetid reefs, endemic Mediterranean bioconstructions widely distributed along the southern part of the basin. As a result, this is a largely-overlooked coastal ecosystem despite the multitude of ecosystem services it provides. The perennial urban development in the Maltese Islands calls for the adoption of urgent action to protect coastal habitats, in particular bioconstructions that increase biodiversity and contribute to mitigating the effects of climate change. The objective of our study was to extensively document the presence and typology of the vermetid reef ecosystems along the coast of Malta and Gozo, assessing the occurrence of putative anthropogenic threats on the same ecosystem. Quantitative measurements were additionally taken to morphologically characterize the recorded bioconstructions. Furthermore, we tested the human pressure effect on the density of vermetid individuals and associated biodiversity. “True” trottoirs were only documented along the south-east coast of Malta, where unfortunately land reclamation projects are expected to be implemented. Although no direct relation between a number of assessed human activities and the density of vermetid individuals was reported in the current study, we suggest the conduction of further studies to investigate the influence of specific disturbances on the conservation status of this ecosystem. This study expands the existing knowledge on the status of vermetid reefs in the Maltese Islands and calls for management and conservation actions to preserve this bioconstruction.peer-reviewe