With their working mechanisms based on ion migration, the switching dynamics and electrical behaviour of memristive devices resemble those of synapses and neurons, making these devices promising candidates for brain-inspired computing. Built into large-scale crossbar arrays to form neural networks, they perform efficient in-memory computing with massive parallelism by directly using physical laws. The dynamical interactions between artificial synapses and neurons equip the networks with both supervised and unsupervised learning capabilities. Moreover, their ability to interface with analogue signals from sensors without analogue/digital conversions reduces the processing time and energy overhead. Although numerous simulations have indicated the potential of these networks for brain-inspired computing, experimental implementation of large-scale memristive arrays is still in its infancy. This Review looks at the progress, challenges and possible solutions for efficient brain-inspired computation with memristive implementations, both as accelerators for deep learning and as building blocks for spiking neural networks.

Memristive crossbar arrays for brain-inspired computing

In this book Professor Holmes discusses some of the evidence relating to one of the most baffling problems yet recognized by biologists-the factors involved in the regulation of growth and form. A wide range of possible influences, from enzymes to cellular competition, is considered. Numerous experiments and theories are described, with or without bibliographic citation. There is a list of references for each chapter and an index. The subject from a scientific standpoint is an exceedingly difficult one, for the reason that very little indeed is understood regarding such phenomena as differentiation. It follows that the problem offers fine opportunities for intellectual jousting by mechanists and vitalists, that hypotheses and theories must often be the weapons of choice, and philosophy the armor. Professor Holmes gives us a good seat from which to watch the combats, explains clearly what is going on, and occasionally slips away to enter the lists himself. This stereoscopic atlas of anatomy was designed as an aid in teaching neuro-anatomy for beginning medical students and as a review for physicians taking Board examinations in Psychiatry and Neurology. Each plate consists of a pair of stereoscopic photographs and a labelled diagram of the important parts seen in the photograph. Perhaps in this day of scarcity of materials, particularly of human brains hardened for dissection, photographs of this kind conceivably can be used as a substitute. Successive stages of dissection are presented in such a fashion that, used in conjunction with the dissecting manual, a student should be able to identify most of the important components of the nervous system without much outside help. The area covered is limited to the gross features of the brain and brain stem and perhaps necessarily does not deal with any of the microscopic structure. So much more can be learned from the dissection of the actual brain that it is doubtful if this atlas would be useful except where brains are not available. A good deal of effort has been spent on the preparation of this atlas, with moderately successful results.

The Integrative Action of the Nervous System.

The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

A comprehensive review on emerging artificial neuromorphic devices

As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

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Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the “no adaptation” and “sensitization” phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots. The development of humanoid robots with artificial intelligence calls for smart solutions for tactile sensing systems that respond to dynamic changes in the environment. Here, Yoon et al. emulate non-adaption and sensitization function of a nociceptor—a sensory neuron—using diffusive oxide-based memristors.

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An artificial nociceptor based on a diffusive memristor.

The advent of memristive devices and the continuing researchand development in the field of neuromorphic computing show great potentialas an alternative to traditional von Neumann computing and bring us evercloser to realizing a true “thinking machine”. Novel neural networkarchitectures and algorithms inspired by the brain are becoming more andmore attractive to keep up with computing needs, relying on intrinsicparallelism and reduced power consumption to outperform more conventionalcomputing methods. This article provides an overview of various neuralnetworks with an emphasis on networks based on memristive emerging devices,with the advantages of memristor neural networks compared with purecomplementary metal oxide semiconductor (CMOS) implementations. A generaldescription of neural networks is presented, followed by a survey ofprominent CMOS networks, and finally networks implemented using emergingmemristive devices are discussed, along with the motivation for developingmemristor based networks and the computational potential these networkspossess.

Vignesh Ravichandran

Papers

An artificial nociceptor based on a diffusive memristor.

Artificial neural networks based on memristive devices