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

On-chip local training is highly desirable for the application of deep neural networks in environment-adaptive edge platforms, which however is hindered by the high time and energy costs of training. Here, we demonstrate efficient training of VGG-16 and LeNet-5 by optimized direct feedback alignment that replaces the layer-by-layer back propagation (BP) of errors. For the first time, the inherent stochasticity in phase change memory fabricated in 40 nm node is exploited to build a merged random feedback matrix with reduced hardware cost. Due to the physical generation of merged matrix and in-memory error computing as well as proposed conductance drift (CD) compensation protocols, the training time and energy consumptions of VGG-16 are reduced by 3× and 3.3×, respectively, compared with hardware-accelerated in-memory BP training, with 90% accuracy on CIFAR-10.

Accelerated Local Training of CNNs by Optimized Direct Feedback Alignment Based on Stochasticity of 4 Mb C-doped Ge 2 Sb 2 Te 5 PCM Chip in 40 nm Node

Spike-Enabled Audio Learning in Multilevel Synaptic Memristor Array-Based Spiking Neural Network

Artificial Astrocyte Memristor with Recoverable Linearity for Neuromorphic Computing

Nonvolatile logic based on memristors provides a promising approach toward efficient Non-von Neumann computing. Here we propose a methodology employing hybrid bipolar-unipolar memristive circuits for efficient in-memory logic functions. By combining the features of bipolar and unipolar memristors, the proposed circuits are able to execute both fundamental Boolean logic and arithmetic computing with high reconfigurability and high parallelism, which compares favorably with existing methods of in-memory logic based on memristors. This work could thus pave the way for the development of new in-memory computing architecture with high efficiency.

Nonvolatile memristor as a new platform for non-von Neumann computing

As conventional flash memory is approaching its fundamental scaling limit, there is an urgent demand for an alternative nonvolatile memory technology at present. Resistance-switching random access memory has attracted extensive interests due to its nonvolatile nature, good scalability, and simple structure. In this work, TiN/ZnO:Mn/Pt junctions, which employ a conductive compound TiN as the top electrode to replace regular metal electrodes, were fabricated and investigated for nonvolatile resistive memory applications. These junctions exhibit bistable resistance state at room temperature, and the devices can be reproducibly switched between the two resistance states by applying bidirectional voltage biases. Moreover, both resistance states are demonstrated to retain for more than 10(4) s without electrical power, demonstrating a nonvolatile nature of the memory device. The mechanism of resistance switching effects in TiN/ZnO:Mn/Pt junctions is interpreted in terms of the drift of oxygen vacancies and the resultant formation/annihilation of local conductive channels through ZnO:Mn/Pt Schottky barrier.

Yuchao Yang

Papers

Accelerated Local Training of CNNs by Optimized Direct Feedback Alignment Based on Stochasticity of 4 Mb C-doped Ge 2 Sb 2 Te 5 PCM Chip in 40 nm Node

Spike-Enabled Audio Learning in Multilevel Synaptic Memristor Array-Based Spiking Neural Network

Artificial Astrocyte Memristor with Recoverable Linearity for Neuromorphic Computing

Nonvolatile memristor as a new platform for non-von Neumann computing

Bipolar resistance switching characteristics in TiN/ZnO:Mn/Pt junctions developed for nonvolatile resistive memory application.