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

/pdf/synaptic-and-neuromorphic-functions-general-discussion-3btbmj3jw4.pdf

Synaptic and neuromorphic functions: general discussion

As an important approach of distributed artificial intelligence, multi-agent system provides an efficient way to solve large-scale computational problems through high-parallelism processing with nonlinear interactions between the agents. However, the huge capacity and complex distribution of the individual agents make it difficult for efficient hardware construction. Here, we propose and demonstrate a multi-agent hardware system that deploys distributed Ag nanoclusters as physical agents and their electrochemical dissolution, growth and evolution dynamics under electric field for high-parallelism exploration of the solution space. The collaboration and competition between the Ag nanoclusters allow information to be effectively expressed and processed, which therefore replaces cumbrous exhaustive operations with self-organization of Ag physical network based on the positive feedback of information interaction, leading to significantly reduced computational complexity. The proposed multi-agent network can be scaled up with parallel and serial integration structures, and demonstrates efficient solution of graph and optimization problems. An artificial potential field with superimposed attractive/repulsive components and varied ion velocity is realized, showing gradient descent route planning with self-adaptive obstacle avoidance. This multi-agent network is expected to serve as a physics-empowered parallel computing hardware. 

/pdf/a-distributed-nanocluster-based-multi-agent-evolutionary-2p5vvuz1.pdf

A distributed nanocluster based multi-agent evolutionary network

Efficiency and privacy-preserving are two key requirements in developing computing systems for processing of privacy-related information. Here, we experimentally demonstrate one AlGaN/GaN high-electron-mobility transistor (HEMT) one Au/NbOx/HfO2/Pt memristor (1HT1R) structure, which features sub-8ns latency, < 96 fJ programming energy, 104s retention at 85 °C and endurance of >106 cycles. We further build a hardware encryptor by combining HEMT-implemented XOR gate and a true random number generator (TRNG) based on variation in Pt/SiOx:Ag/Pt memristor. The proposed TRNG is able to generate 0.48Mb random bitstreams with ultralow energy consumption of 50fJ/bit. A memristors-based homomorphic encryption system is thus constructed based on the 1HT1R and hardware encryptor, capable of performing image segmentation with ultrafast speed, low energy consumption, and privacy-preserving capability.

1-HEMT-1-Memristor With Hardware Encryptor for Privacy-Preserving Image Processing

Neural ordinary differential equation (Neural-ODE) outperforms conventional deep neural networks (DNNs) in modeling continuous-time or dynamical systems by adopting numerical ODE integration onto a shallow embedded NN. However, Neural-ODE suffers from slow inference due to the costly iterative stepsize search in numerical integration, especially when using higher-order Runge-Kutta (RK) methods and smaller error tolerance for improved integration accuracy. In this work, we first present algorithmic techniques to speedup RK-based Neural-ODE inference: a two-stage coarse-grained/fine-grained structured pruning method based on top-K sparsification that reduces the overall computations by more than 60% in the embedded NN and a history-based stepsize search method based on past integration steps that reduces the latency for reaching accepted stepsize by up to 77% in RK methods. A reconfigurable hardware architecture is co-designed based on proposed speedup techniques, featuring three processing loops to support programmable embedded NN and a variety of higher-order RK methods. Sparse activation processor with multi-dimensional sorters is designed to exploit structured sparsity in activations. Implemented on a Xilinx Virtex-7 XC7VX690T FPGA and experimented on a variety of datasets, the prototype accelerator using a more complex 3rd-order RK method achieves more than 2.6x speedup compared to the latest Neural-ODE FPGA accelerator using the simplest Euler method. Compared to a software execution on Nvidia A100 GPU, the inference speedup can be up to 18x.

Accelerating Neural-ODE Inference on FPGAs with Two-Stage Structured Pruning and History-based Stepsize Search

/pdf/a-distributed-nanocluster-based-multi-agent-evolutionary-12sksq69.pdf

Yuchao Yang

Papers

Synaptic and neuromorphic functions: general discussion

A distributed nanocluster based multi-agent evolutionary network

1-HEMT-1-Memristor With Hardware Encryptor for Privacy-Preserving Image Processing

Accelerating Neural-ODE Inference on FPGAs with Two-Stage Structured Pruning and History-based Stepsize Search

A distributed nanocluster based multi-agent evolutionary network