Neuromorphic computing at scale.

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphic designers apply the principles of biointelligence discovered by neuroscientists to design efficient computational systems, often for applications with size, weight and power constraints. With this research field at a critical juncture, it is crucial to chart the course for the development of future large-scale neuromorphic systems.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies.

We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes.

Neuromorphic Decoding of Spinal Motor Neuron Behaviour During Natural Hand Movements for a New Generation of Wearable Neural Interfaces.

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system.

Insights into Batch Selection for Event-Camera Motion Estimation.

Event cameras measure scene changes with high temporal resolutions, making them well-suited for visual motion estimation. The activation of pixels results in an asynchronous stream of digital data (events), which rolls continuously over time without the discrete temporal boundaries typical of frame-based cameras (where a data packet or frame is emitted at a fixed temporal rate). As such, it is not trivial to define how to group/accumulate events in a way that is sufficient for computation.

Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware.

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required.

The comet assay for the evaluation of gut content genotoxicity: Use in human studies as an early biomarker of colon cancer risk.

This short narrative review describes the use of the comet assay to evaluate the formation of genotoxic compounds in the gut lumen in human studies. The fecal water genotoxicity assay is based on ability of the gut content to induce genotoxicity in a cellular model, employing the aqueous component of the feces (fecal water) as this is supposed to contain most of the reactive species and to convey them to the intestinal epithelium. This non-invasive and low-cost assay has been demonstrated to be associated with colon cancer risk in animal models, and although the final validation against human tumors is lacking, it is widely used as a colo-rectal cancer risk biomarker in human nutritional intervention studies.

Event-driven proto-object based saliency in 3D space to attract a robot's attention.

To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that produce synchronous frames.

Embodied neuromorphic intelligence.

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society.

luvHarris: A Practical Corner Detector for Event-Cameras.

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput.

Rotational thromboelastometry for diagnosis of fibrinolysis induced by urokinase in an in-vitro model.

Fibrinolysis can be abnormally activated in several critical care settings but it's often misdiagnosed by standard laboratory tests. Although rotational thromboelastometry can assess the whole coagulative process, its ability to detect fibrinolysis has been questioned. Aim of this study was to investigate the ability of thromboelastometry in detecting induced fibrinolysis in an in-vitro model.

Face Pose Alignment with Event Cameras.

Event camera (EC) emerges as a bio-inspired sensor which can be an alternative or complementary vision modality with the benefits of energy efficiency, high dynamic range, and high temporal resolution coupled with activity dependent sparse sensing. In this study we investigate with ECs the problem of face pose alignment, which is an essential pre-processing stage for facial processing pipelines. EC-based alignment can unlock all these benefits in facial applications, especially where motion and dynamics carry the most relevant information due to the temporal change event sensing.

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphery to central processors, at the cost of wiring, latency, fault tolerance and robustness. We envision a decentralized approach where intelligence is embedded in the sensing nodes, using a unique neuromorphic methodology to extract relevant information in robotic skins. Here we specifically address pain perception and the association of nociception with tactile perception to trigger the escape reflex in a sensorized robotic arm.

Event-Based Vision: A Survey.

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range.

An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot.

In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. The neuromorphic vision system of the iCub is used to correct for drift in the pose estimation.

Event-Based Eccentric Motion Detection Exploiting Time Difference Encoding.

Attentional selectivity tends to follow events considered as interesting stimuli. Indeed, the motion of visual stimuli present in the environment attract our attention and allow us to react and interact with our surroundings. Extracting relevant motion information from the environment presents a challenge with regards to the high information content of the visual input.

Blood flow maintenance by cardiac massage during cardiopulmonary resuscitation: Classical theories, newer hypotheses, and clinical utility of mechanical devices.

The mechanisms by which closed chest cardiac massage produces and maintains blood flow during cardiopulmonary resuscitation are still debated. To date, two main theories exist: the "cardiac pump", which assumes that blood flow is driven by direct cardiac compression and the "chest pump", which hypothesizes that blood flow is caused by changes in intrathoracic pressure. Newer hypotheses including the "atrial pump", the "lung pump", and the "respiratory pump" were also proposed.

An Ultralow Leakage Synaptic Scaling Homeostatic Plasticity Circuit With Configurable Time Scales up to 100 ks.

Homeostatic plasticity is a stabilizing mechanism commonly observed in real neural systems that allows neurons to maintain their activity around a functional operating point. This phenomenon can be used in neuromorphic systems to compensate for slowly changing conditions or chronic shifts in the system configuration. However, to avoid interference with other adaptation or learning processes active in the neuromorphic system, it is important that the homeostatic plasticity mechanism operates on time scales that are much longer than conventional synaptic plasticity ones.

iCub: The not-yet-finished story of building a robot child.

The iCub open-source humanoid robot child is a successful initiative supporting research in embodied artificial intelligence.

Quantitative analysis of epicardial fat volume: effects of scanning protocol and reproducibility of measurements in non-contrast cardiac CT coronary CT angiography.

Background: Several studies have focused on the role of epicardial fat in the pathogenesis of cardiovascular disease (CVD). The main purpose of the study was to evaluate a computerized method for the quantitative analysis of epicardial fat volume (EFV) by non-contrast cardiac CT (NCT) for coronary calcium scan and coronary CT angiography (coronary CTA).

Methods: Thirty patients (61±12.

Safety and feasibility of electrochemotherapy in patients with unresectable colorectal liver metastases: A pilot study.

Background And Objectives: Electrochemotherapy is a novel ablation technique combining chemotherapeutic agents with reversible cell membrane electroporation. Previous experiences have shown its efficacy for cutaneous tumors. Its application for deep-seated malignancies is under investigation.