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Inspired by human brain, neuromorphic computing technologies have made important breakthroughs in recent years as alternatives to overcome the power and latency shortfalls of traditional digital computing. An interdisciplinary approach is being taken to address the challenge of creating more efficient and intelligent computing systems that can perform diverse tasks, to design hardware with increasing complexity from single device to system architecture level, and to develop new theories and brain-inspired algorithms for future computing.
Edge and High-Performance Computing, Bio-Signal Processing and Brain-Computer Interface
We welcome the submissions of primary research that fall into any of the above-mentioned categories. All the submissions will be subject to the same peer review process and editorial standard as regular Nature Communications articles.
Designing bio-inspired multisensory neurons remains a challenge. Here, the authors develop an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor capable of super-additive response, inverse effectiveness effect, and temporal congruency.
Next-generation human-machine interfaces require efficient physiological signal processing systems. Here, the authors propose a hardware system that uses VO2 memristors to perform brain-like encoding and analysis of physiological signals, and is capable of identifying arrhythmia and epileptic seizures.
Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing. Here the authors, develop a neuro-inspired optical sensor based on NbS2/MoS2 films that can operate with monolithically integrated functions of static image enhancement and dynamic trajectory registration.
Implementing emotional aspects like physiology and psychology in decision-making remains a challenge. Here, the authors propose a bio-inspired gustatory circuit based on 2D materials that mimics adaptive feeding behavior in humans, considering both physiological states (hunger) and psychological states (appetite).
Designing optoelectronic synapses having a multispectral color-discriminating ability is crucial for neuromorphic visual system. Here, the authors propose an strategy to introduce RGB color-discriminating synaptic functionality into a 2-terminals memristor regardless of switching medium and design a color image-recognizing CNN and light-programmable reservoir computing.
Designing full-color spherical artificial eyes remains a challenge. Here, Long et al. report a bionic eye where each pixel on the hemispherical retina can recognize different colors based on the unique bidirectional photo response; with optical adaptivity and neuromorphic preprocessing ability
Designing efficient photonic neuromorphic systems remains a challenge. Here, the authors develop an in-sensor Reservoir Computing system for multi-tasked pattern classification based on a light-responsive semiconducting polymer (p-NDI) with efficient exciton dissociations, charge trapping capability, and through-space charge-transport characteristics.
Developing an artificial olfactory system that can mimic the biological functions remains a challenge. Here, the authors develop an artificial chemosensory synapse based on a flexible organic electrochemical transistor gated by the potential generated by the interaction of gas molecules with ions in a chemoreceptive ionogel.
Designing machine learning hardware on flexible substrates is promising for several applications. Here, the authors propose an integrated smart system built with low-cost flexible electronics components for classifying human malodour, and demonstrates that the proposed system scores malodour as good as expert human assessors.
Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.
Wearable sensors with edge computing are desired for human motion monitoring. Here, the authors demonstrate a topographic design for wearable MXene sensor modules with wireless streaming or in-sensor computing models for avatar reconstruction.
Designing wereable neural invasive electrical stimulation system remains a challenge. Here, researchers provide an effective technology platform for the elimination of tricky neural stimulus-inertia using bionic electronic modulation, which is a significant step forward for long-lasting treatment of nervous system diseases.
Designing efficient bio-inspired vision systems remains a challenge. Here, the authors report a bio-inspired striate visual cortex with binocular and orientation selective receptive field based on self-powered memristor to enable machine vision with brisk edge and corner detection in the future applications.
Designing an efficient platform that enables verbal communication without vocalization remains a challenge. Here, the authors propose a silent speech interface by utilizing a deep learning algorithm combined with strain sensors attached near the subject’s mouth, able to collect 100 words and classify at a high accuracy rate.
With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental von Neumann bottlenecks. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity.
The adoption of photonic synapses with biosimilarity to realize analog signal transmission is of significance in realizing artificial illuminance modulation responses. Here, the authors report a biomimetic ocular prosthesis system based on quantum dots embedded photonic synapses with improved depression properties through mid-gap trap.
Tactile sensors in human-machine interaction systems can provide precise input signals and the necessary feedback between humans and machines. Here, the authors developed a black phosphorous-based tactile sensor array system that can provide touch into audio feedback.
Designing efficient brain-inspired electronics remains a challenge. Here, Liu et al. develop a flexible perovskite-based artificial synapse with low energy consumption and fast response frequency and realize an artificial neuromuscular system with muscular-fatigue warning.
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing energy consumption of 1.9 fJ/spike.
Designing efficient sensing-memory-computing systems remains a challenge. Here, the authors propose a self-powered vertical tribo-transistor based on MXenes to implement the multi-sensing-memory-computing function and the interaction of multisensory integration.
While great progress has been made in object recognition, implementing them is typically based on conventional electronic hardware. Here the authors introduce a concept of neuro-metamaterials that enable a dynamic entirely-optical object recognition and mirage.
The real-world object localization application needs a low-latency and power efficient computing system. Here, Moro et al. demonstrate a neuromorphic in-memory event driven system, inspired by the barn owl’s neuroanatomy, which is orders of magnitude more energy efficient than microcontrollers.
The scalability of neuromorphic devices depends on the dismissal of capacitors and additional circuits. Here Liu et al. report an artificial neuron based on the polarization and depolarization of an anti-ferroelectric film, avoiding additional elements and reaching 37 fJ/spike of power consumption.
Traditional learning procedures for artificial intelligence rely on digital methods not suitable for physical hardware. Here, Nakajima et al. demonstrate gradient-free physical deep learning by augmenting a biologically inspired algorithm, accelerating the computation speed on optoelectronic hardware.
Circularly polarized light adds a unique dimension to optical information processing and communication. Here, the authors present a development of a photonic artificial synapse device using chiral perovskite hybrid materials and carbon nanotubes. The heterostructure exhibits efficient synaptic and neuromorphic behaviors, enabling accurate recognition of circularly polarized images.
Existing artificial corneas can assume partial functions of the human cornea, but sense reconstruction remains a challenge. Qu et al. develop an artificially-intelligent cornea with tactile sensation that enables sensory expansion and interaction.
Existing solutions based Advanced Encryption Standard to address the security issues of nonvolatile memories incurs significant performance and power overhead. Here, the authors propose a lightweight XOR-gate based encryption/decryption technique by exploiting in-situ array operations, which achieves significant area/latency/power reduction compared to conventional designs.
Ferroelectric transistors are promising building blocks for developing energy-efficient memory and logic applications. Here, the authors report a record high 300 K resistance on-off ratio achieved in ferroelectric-gated Mott transistors by exploiting a charge transfer layer to tailor the channel carrier density and mitigate the ferroelectric depolarization effect.
Designing efficient nanoscale and adaptable bioinspired memristors remains a challenge. Here, the authors develop a bioinspired hydrophobically gated memristive nanopore capable of learning, forgetting, and retaining memory through an electrowetting mechanism.
The ability of living systems to process signals and information is of vital importance. Inspired by nature, Wang and Cichos show an experimental realization of a physical reservoir computer using self-propelled active microparticles to predict chaotic time series such as the Mackey–Glass and Lorenz series.
Bao et al. report a neuromorphic bionic electro-stimulation solution based on atomic-scale semiconductor floating-gate memory circuit, which enables efficient inhibition of acute inflammation with low stimulation currents that are damage-free to neurons.
Parallel information transmission components and hardware strategies are still lacking in neural networks. Here, the authors propose a strategy to use light emitting memristors with negative ultraviolet photoconductivity and intrinsic parallelism to construct direct information cross-layer modules.
Artificial sensory systems are often limited in structure and functionality. Here, Jiang et al. report a neuromorphic antennal sensory system that achieves spatiotemporal perception of vibrotactile and magnetic stimuli, showcasing biomimetic perceptual intelligence.
All-in-one multi-task photoperception is desirable for artificial vision systems. Wen et al. present wafer-scale high density integration of artificial photoreceptors that combine photoadaptation and circular polarized light vision, enabled by chiral-nanocluster-conjugated molecule heterostructures.
The communication of colour information stands as one of the most immediate and widespread methods of interaction among biological entities. Xu et al. report an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for real-time visualised in-sensor computing.
Photonic Stochastic Emergent Storage is a neuromorphic photonic device for image storage and classification based on scattering-intrinsic patterns. Here, the authors show emergent storage employs stochastic prototype scattering-induced light patterns to generate categories corresponding to emergent archetypes.