NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community

Cabbage and fermented vegetables : From death rate heterogeneity in countries to candidates for mitigation strategies of severe COVID-19

Large differences in COVID-19 death rates exist between countries and between regions of the same country. Some very low death rate countries such as Eastern Asia, Central Europe, or the Balkans have a common feature of eating large quantities of fermented foods. Although biases exist when examining ecological studies, fermented vegetables or cabbage have been associated with low death rates in European countries. SARS-CoV-2 binds to its receptor, the angiotensin-converting enzyme 2 (ACE2). As a result of SARS-CoV-2 binding, ACE2 downregulation enhances the angiotensin II receptor type 1 (AT(1)R) axis associated with oxidative stress. This leads to insulin resistance as well as lung and endothelial damage, two severe outcomes of COVID-19. The nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is the most potent antioxidant in humans and can block in particular the AT(1)R axis. Cabbage contains precursors of sulforaphane, the most active natural activator of Nrf2. Fermented vegetables contain many lactobacilli, which are also potent Nrf2 activators. Three examples are: kimchi in Korea, westernized foods, and the slum paradox. It is proposed that fermented cabbage is a proof-of-concept of dietary manipulations that may enhance Nrf2-associated antioxidant effects, helpful in mitigating COVID-19 severity.Peer reviewe

Nrf2-interacting nutrients and COVID-19 : time for research to develop adaptation strategies

There are large between- and within-country variations in COVID-19 death rates. Some very low death rate settings such as Eastern Asia, Central Europe, the Balkans and Africa have a common feature of eating large quantities of fermented foods whose intake is associated with the activation of the Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) anti-oxidant transcription factor. There are many Nrf2-interacting nutrients (berberine, curcumin, epigallocatechin gallate, genistein, quercetin, resveratrol, sulforaphane) that all act similarly to reduce insulin resistance, endothelial damage, lung injury and cytokine storm. They also act on the same mechanisms (mTOR: Mammalian target of rapamycin, PPAR gamma:Peroxisome proliferator-activated receptor, NF kappa B: Nuclear factor kappa B, ERK: Extracellular signal-regulated kinases and eIF2 alpha:Elongation initiation factor 2 alpha). They may as a result be important in mitigating the severity of COVID-19, acting through the endoplasmic reticulum stress or ACE-Angiotensin-II-AT(1)R axis (AT(1)R) pathway. Many Nrf2-interacting nutrients are also interacting with TRPA1 and/or TRPV1. Interestingly, geographical areas with very low COVID-19 mortality are those with the lowest prevalence of obesity (Sub-Saharan Africa and Asia). It is tempting to propose that Nrf2-interacting foods and nutrients can re-balance insulin resistance and have a significant effect on COVID-19 severity. It is therefore possible that the intake of these foods may restore an optimal natural balance for the Nrf2 pathway and may be of interest in the mitigation of COVID-19 severity

Oxidative and DNA damage in obese patients undergoing bariatric surgery: A one-year follow-up study

The pathogenesis of obesity and related comorbidities has long been associated with oxidative stress. The excess of adipose tissue contributes to the production of free radicals that sustain both a local and a systemic chronic inflammatory state, whereas its reduction can bring to an improvement in inflammation and oxidative stress. In our work, using the fluorescent lipid probe BODIPY® 581/591 C11 and the γH2AX foci assay, a well-known marker of DNA double strand breaks (DSB), we evaluated the extent of cell membrane oxidation and DNA damage in peripheral blood lymphocytes of normal weight (NW) controls and obese patients sampled before and after bariatric surgery. Compared to NW controls, we observed a marked increase in both the frequencies of oxidized cells or nuclei exhibiting phosphorylation of histone H2AX in preoperatory obese patients. After bariatric surgery, obese patients, resampled over one-year follow-up, improved oxidative damage and reduced the presence of DSB. In conclusion, the present study highlights the importance for obese patients undergoing bariatric surgery to also monitor these molecular markers during their postoperative follow-up

Jointly efficient encoding and decoding in neural populations.

The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data

Benchmarking of hardware-efficient real-time neural decoding in brain–computer interfaces

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain–computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction

UCTD and SLE patients show increased levels of oxidative and DNA damage together with an altered kinetics of DSB repair

: Immunological tolerance is a critical feature of the immune system; its loss might lead to an abnormal response of lymphocytes causing autoimmune diseases. One of the most important groups belonging to autoimmune disorders is the connective tissue diseases (CTD). CTD are classified among systemic rheumatic diseases and include pathologies such as systemic lupus erythematosus (SLE), and undifferentiated CTD (UCTD). In this study, we evaluated oxidative and genome damage in peripheral blood lymphocytes from patients with SLE and UCTD, further classified on the basis of disease activity and the presence/absence of a serological profile. Oxidative damage was evaluated in cell membrane using the fluorescent fatty acid analogue BODIPY581/591 C11. The percentage of oxidised lymphocytes in both SLE and UCTD patients was higher than in the control group, and the oxidative stress correlated positively with both disease activity and autoantibody profile. The γH2AX focus assay was used to quantify the presence of spontaneous double strand breaks (DSBs), and to assess the abilities of DSBs repair system after T cells were treated with mitomycin C (MMC). Subjects with these autoimmune disorders showed a higher number of γH2AX foci than healthy controls, but no correlation with diseases activity and presence of serological profile was observed. In addition, patients displayed an altered response to MMC-induced DSBs, which led their peripheral cells to greatly increase apoptosis. Taken together our results confirmed an interplay among oxidative stress, DNA damage and impaired DNA repair, which are directly correlated to the aggressiveness and clinical progression of the diseases. We propose the evaluation of these molecular markers to better characterise SLE and UCTD, aiming to improve the treatment plan and the quality of the patients' life

High level of γH2AX phosphorylation in the cord-blood cells of large-for-gestational-age (LGA) newborns

Newborns can experience adverse effects as a consequence of maternal or in utero exposure, altered growth of the fetus, or placental dysfunctions. Accurate characterization of gestational age allows monitoring of fetal growth, identification of deviations from the normal growth trajectory, and classification of babies as adapted, small, or large for gestational age (AGA, SGA, or LGA). The aim of this work was to evaluate nuclear and oxidative damage in umbilical cord-blood cells of newborns (sampled at birth), by applying the gamma H2AX assay and the fluorescent probe BODIPY581/591 C-11, to detect DNA DSB and cell membrane oxidation, respectively. No statistically sig-nificant differences were observed in the proportion of oxidized cord-blood cells among the groups of newborns, although the LGA group showed the highest value. With regard to genome damage, elevated levels of gamma H2AX foci were detected in the cell nuclei from LGA newborns as compared to AGA or SGA babies, whose values did not differ from each other. Considering that the observed DNA damage, although still repairable, can represent a risk factor for obesity, metabolic diseases, or other pathologies, monitoring genome and cell integrity at birth can provide useful information for prevention of diseases later in life

Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG

Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the correct diagnosis of BrS and its differentiation from other pathologies.This work presents an application of Echo State Networks (ESN) in the Recurrent Neural Networks (RNN) class for diagnosing BrS from the ECG time series.12-lead ECGs were obtained from patients with a definite clinical diagnosis of spontaneous BrS Type 1 pattern (Group A), patients who underwent provocative pharmacological testing to induce BrS type 1 pattern, which resulted in positive (Group B) or negative (Group C), and control subjects (Group D). One extracted beat in the V2 lead was used as input, and the dataset was used to train and evaluate the ESN model using a double cross-validation approach. ESN performance was compared with that of 4 cardiologists trained in electrophysiology.The model performance was assessed in the dataset, with a correct global diagnosis observed in 91.5 % of cases compared to clinicians (88.0 %). High specificity (94.5 %), sensitivity (87.0 %) and AUC (94.7 %) for BrS recognition by ESN were observed in Groups A + B vs. C + D.Our results show that this ML model can discriminate Type 1 BrS ECGs with high accuracy comparable to expert clinicians. Future availability of larger datasets may improve the model performance and increase the potential of the ESN as a clinical support system tool for daily clinical practice