Event-based computation: unsupervised elementary motion decomposition

Fast, localised motion detection is crucial for an efficient attention mechanism. We show that modelling a network capable of such motion detection can be performed using spiking neural networks simulated on many-core neuromorphic hard-ware. Moreover, highly sensitive neurons arise from the presented network architecture through unsupervised self-organisation. We use a synaptic rewiring rule which has been shown to enable the formation and refinement of neural topographic maps. Our extension allows newly formed synapses to be initialised with a delay drawn from a uniform distribution. Repeated exposure to moving bars enables neurons to be sensitised to a preferred direction of movement. Incorporating heterogeneous delays results in more sensitive neural responses. A readout mechanism involving a neuron for each learnt motion is sufficient to establish the input stimulus class

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

Contrast enhanced ultrasound of renal masses. A reappraisal of EFSUMB recommendations and possible emerging applications

Abstract The main imagistic method for characterization of renal lesions is contrast enhanced computed tomography (CECT). Disadvantages of CECT are a contrast-induced nephropathy in patients with renal impairment, allergic reactions and high costs. Contrast-enhanced ultrasound (CEUS) evaluation of hepatic and non-hepatic lesions is a relatively new, but increasingly utilised, diagnostic method. In 2011 the European Federation of Societies of Ultrasound in Medicine and Biology (EFSUMB) updated the Guidelines and Recommendations on the Clinical Practice of CEUS and included in the recommendation the renal pathology. However, there are several possible new indications that have not been discussed (pyelocaliceal masses and renal vein thrombosis) and several issues that remain controversial such as the differentiation of benign and malignant tumours or the differentiation of lymphoma and metastasis. This study aims to review literature data, as well as reveal the latest findings in the field of renal CEUS

Understanding the Role of Non-Coding RNAs in Bladder Cancer: From Dark Matter to Valuable Therapeutic Targets

The mortality and morbidity that characterize bladder cancer compel this malignancy into the category of hot topics in terms of biomolecular research. Therefore, a better knowledge of the specific molecular mechanisms that underlie the development and progression of bladder cancer is demanded. Tumor heterogeneity among patients with similar diagnosis, as well as intratumor heterogeneity, generates difficulties in terms of targeted therapy. Furthermore, late diagnosis represents an ongoing issue, significantly reducing the response to therapy and, inevitably, the overall survival. The role of non-coding RNAs in bladder cancer emerged in the last decade, revealing that microRNAs (miRNAs) may act as tumor suppressor genes, respectively oncogenes, but also as biomarkers for early diagnosis. Regarding other types of non-coding RNAs, especially long non-coding RNAs (lncRNAs) which are extensively reviewed in this article, their exact roles in tumorigenesis are—for the time being—not as evident as in the case of miRNAs, but, still, clearly suggested. Therefore, this review covers the non-coding RNA expression profile of bladder cancer patients and their validated target genes in bladder cancer cell lines, with repercussions on processes such as proliferation, invasiveness, apoptosis, cell cycle arrest, and other molecular pathways which are specific for the malignant transformation of cells