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

Memristive Devices and Networks for Brain-Inspired Computing

The field of neuromorphic computing systems has been through enormous progress in recent years, whereas some issues are still remaining to be solved. One of the biggest challenges in neuromorphic circuit designing is the lack of a robust device with functions comparable to or even better than the metal-oxide-semiconductor field-effect transistor (MOSFET) used in traditional integrated circuits. In this work, we demonstrated a MoS2 neuristor using a dual-gate transistor structure. An ionic top gate is designed to control the migration of ions, while an electronic back gate is used to control electronic migration. By applying different driving signals, the MoS2 neuristor can be programmed as a neuron, a synapse, or an n-type MOSFET, which can be seen as a fundamental building block in the neuromorphic circuit design. The MoS2 neuristor provides viable solutions for future reconfigurable neuromorphic systems and can be a promising candidate for future neuromorphic computing.

Dual-Gated MoS2 Neuristor for Neuromorphic Computing

The development of smart, scalable, and power efficient computers relies on innovative technologies that can distribute memory alongside processing, such as emerging neuromorphic computing and in-memory logic technologies. Here, a type of vertical 3-terminal oxide based nanoionic device capable of implementing heterosynaptic plasticity and nonvolatile Boolean logic simultaneously is demonstrated. The heterosynaptic plasticity endows the devices with facilely tunable synaptic kinetics via tailoring modulatory signals, which is shown to be crucial for achieving optimized learning scheme, therefore offering promising building blocks for neuromorphic computing. Furthermore, it is demonstrated that these heterosynaptic devices can simultaneously be utilized to implement nonvolatile Boolean logic with improved efficiency compared with existing approaches, therefore implying in-memory computing potentialities. The heterosynaptic devices with such multifunctionality thus hold great potential for next-generation non-von Neumann computing applications.

Multifunctional Nanoionic Devices Enabling Simultaneous Heterosynaptic Plasticity and Efficient In-Memory Boolean Logic

Low Power Parylene‐Based Memristors with a Graphene Barrier Layer for Flexible Electronics Applications

In this letter, the impacts introduced by oxygen-scavenging metal (Ti), including forming voltages variation and resistances dispersion in high/low states of HfO x -based RRAM devices, were investigated. Based on experiments, it is concluded that Ti atoms during physical vapor deposition process can randomly penetrate into the bulk of HfO x and are intended to randomly create the initial oxygen vacancies, thus causing the performance degradation. Moreover, a novel structure with an atomic layer deposition-TiN buffer layer inserted between oxygen-scavenging Ti and HfO x layers was proposed and experimentally demonstrated to reduce the impacts of Ti penetration, which further improves vacancy-creating approach. It was experimentally verified that with the help of incorporating a TiN buffer layer, the uniformity of switching parameters, such as forming voltage and resistance, was effectively improved. The understanding and improvement of device variation caused by oxygen-scavenging metal in oxides-based resistive random access memory (RRAM) are useful for further development of highly reliable RRAM technology.

Yuchao Yang

Papers

Memristive Devices and Networks for Brain-Inspired Computing

Dual-Gated MoS2 Neuristor for Neuromorphic Computing

Multifunctional Nanoionic Devices Enabling Simultaneous Heterosynaptic Plasticity and Efficient In-Memory Boolean Logic

Low Power Parylene‐Based Memristors with a Graphene Barrier Layer for Flexible Electronics Applications

Improvement of HfO x -Based RRAM Device Variation by Inserting ALD TiN Buffer Layer