I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The organization of behavior: A neuropsychological theory

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Memristive devices for computing

Resistance switching in metal oxides could form the basis for next-generation non-volatile memory. It has been argued that the current in the high-conductivity state of several technologically relevant oxide materials flows through localized filaments, but these filaments have been characterized only indirectly, limiting our understanding of the switching mechanism. Here, we use high-resolution transmission electron microscopy to probe directly the nanofilaments in a Pt/TiO2/Pt system during resistive switching. In situ current–voltage and low-temperature (∼130 K) conductivity measurements confirm that switching occurs by the formation and disruption of TinO2n−1 (or so-called Magneli phase) filaments. Knowledge of the composition, structure and dimensions of these filaments will provide a foundation for unravelling the full mechanism of resistance switching in oxide thin films, and help guide research into the stability and scalability of such films for applications. Nanoscale filaments with a Magneli structure are shown to be responsible for resistance switching in thin films of TiO2, and the properties of the filaments are directly observed during the switching process.

Atomic structure of conducting nanofilaments in TiO2 resistive switching memory

We developed two-step solution-phase reactions to form hybrid materials of Mn3O4 nanoparticles on reduced graphene oxide (RGO) sheets for lithium ion battery applications. Mn3O4 nanoparticles grown selectively on RGO sheets over free particle growth in solution allowed for the electrically insulating Mn3O4 nanoparticles wired up to a current collector through the underlying conducting graphene network. The Mn3O4 nanoparticles formed on RGO show a high specific capacity up to ~900mAh/g near its theoretical capacity with good rate capability and cycling stability, owing to the intimate interactions between the graphene substrates and the Mn3O4 nanoparticles grown atop. The Mn3O4/RGO hybrid could be a promising candidate material for high-capacity, low-cost, and environmentally friendly anode for lithium ion batteries. Our growth-on-graphene approach should offer a new technique for design and synthesis of battery electrodes based on highly insulating materials.

Mn3O4-Graphene Hybrid as a High Capacity Anode Material for Lithium Ion Batteries

Inspired by the computing architecture of human brain, neuromorphic computing promises massively parallel, energy efficient and fault tolerant computation compared with conventional von Neumann approaches. In order to achieve this ambitious goal, one of the crucial tasks is to make devices that can emulate the functions of biological synapses at the physical level. Here we fabricated a novel Pt/TaOx/Ta memristor with recessed Ta bottom electrodes, where the TaOx film was formed by thermal oxidation of the bottom electrode and thus had a constantly ascending concentration of oxygen vacancies from the Pt/TaOx interface to the TaOx/Ta interface. Such cell geometry and ion distribution profile gave rise to highly incremental and uniform resistive switching that emulated the potentiation or depression process of synapses. Furthermore, important synaptic learning rules, such as spike timing dependent plasticity, could also be implemented by these devices, making them well suited for electronic synapses in neuromorphic systems.

TaOx based memristors with recessed bottom electrodes and built-in ion concentration gradient as electronic synapses

Unlabeled data processing is of great significance for artificial intelligence (AI), since well-structured labeled data are scarce in a majority of practical applications due to the high cost of human annotation of labeling data. Therefore, automatous analysis of unlabeled datasets is important, and relevant algorithms for processing unlabeled data, such as k-means clustering, restricted Boltzmann machine and locally competitive algorithms etc, play a critical role in the development of AI techniques. Memristive devices offer potential for power and time efficient implementation of unlabeled data processing due to their unique properties in neuromorphic and in-memory computing. This review provides an overview of the design principles and applications of memristive devices for various unlabeled data processing and cognitive AI tasks.

https://iopscience.iop.org/article/10.1088/2634-4386/ac734a/pdf

Memristive devices based hardware for unlabeled data processing

Continuous attractor neural network (CANN) is a canonical model for neural information representation and processing, which has been applied to describe the encoding of continuous features, such as orientation, head direction and spatial location in neural systems. Specifically, theoretical studies based on a firing-rate model have found that a CANN with negative feedback, such as spike frequency adaptation (SFA), has the capability of tracking a continuously moving stimulus anticipatively. In this study, facing the booming development of neuromorphic computing using spiking neural networks (SNNs), we built a spiking continuous attractor neural network (S-CANN) with SFA to implement anticipative tracking. Further, we simplified the model, in terms of connection weights, external inputs, and network size, to facilitate its implementation with neuromorphic hardware.

Spiking Continuous Attractor Neural Networks with Spike Frequency Adaptation for Anticipative Tracking

Here we report threshold switching characteristics in niobium oxide films, which is in turn used to build an oscillation neuron. In particular, we show that strong correlation exists between the oxygen flow ratio during NbOx film deposition and the electrical properties of NbOx devices. The NbOx devices can show metallic, volatile threshold switching, and non-volatile resistive switching characteristics, depending on the oxygen flow ratio. The NbOx device with threshold switching can be used to form an oscillation neuron displaying three critical features: all-or-nothing oscillation, threshold-driven oscillation and input-modulated frequency response, which could be promising for applications as artificial neurons in hardware neural networks.

Oscillation neuron based on threshold switching characteristics of niobium oxide films

In the above paper [1] , there is an error in the information of corresponding author’s email address. The corrected corresponding author’s email address information is underlined and shown as follows:

Corrections to “A Novel Tunnel FET Design Through Adaptive Bandgap Engineering With Constant Sub-Threshold Slope Over 5 Decades of Current and High ${I_{{\mathrm {ON}}}}/{I_{{\mathrm {OFF}}}}$ Ratio”

Yuchao Yang

Papers

TaOx based memristors with recessed bottom electrodes and built-in ion concentration gradient as electronic synapses

Memristive devices based hardware for unlabeled data processing

Spiking Continuous Attractor Neural Networks with Spike Frequency Adaptation for Anticipative Tracking

Oscillation neuron based on threshold switching characteristics of niobium oxide films

Corrections to “A Novel Tunnel FET Design Through Adaptive Bandgap Engineering With Constant Sub-Threshold Slope Over 5 Decades of Current and High ${I_{{\mathrm {ON}}}}/{I_{{\mathrm {OFF}}}}$ Ratio”