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

Recent advances in physical reservoir computing: A review

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function. Reservoir computing facilitates the projection of temporal input signals onto a high-dimensional feature space via a dynamic system, known as the reservoir. Du et al. realise this concept using metal-oxide-based memristors with short-term memory to perform digit recognition tasks and solve non-linear problems.

/pdf/reservoir-computing-using-dynamic-memristors-for-temporal-3oa1ywwgr9.pdf

Reservoir computing using dynamic memristors for temporal information processing

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Digital Design And Computer Architecture

FPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors

Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons

Low power CMOS power amplifier design for RFID and the Internet of Things

This paper proposes an equivalent lumped element model for the differential Through Silicon Vias (TSVs) with considering the effect of voltage dependent and nonlinear capacitance. The modeling and analysis of the differential signaling with TSVs play a critical role in designing the high performance TSV channel in the three dimensional integrated circuit (3D IC). TSVs have been mostly modeled assuming that the TSV metal insulator semiconductor (MIS) interface is not biased and the silicon substrate is a lossy, low conductive medium. Ignoring the semiconductor properties of the substrate and the resulting MOS capacitance introduce significant inaccuracies in the TSV modeling. In this paper, we investigate the complementary nature of differential signals which introduces a virtual ground and automatically biases the TSV MIS interface, causing carrier accumulation and depletion. Furthermore, the large digital signal swing makes the depletion region change the depletion width dynamically, which introduces a nonlinear and large signal TSV capacitance. The capacitance is modeled analytically and a new equivalent circuit model for the differential TSVs is proposed accordingly. The impact of the voltage dependent and nonlinear capacitance on the performance of high speed differential signals is analyzed through channel simulations with eye diagram approach.

/pdf/modeling-differential-through-silicon-vias-tsvs-with-voltage-41urp1snoa.pdf

Modeling Differential Through-Silicon-Vias (TSVs) with Voltage Dependent and Nonlinear Capacitance

In this work, an extensively comparative study is conducted to demonstrate the performance of Particle Swarm Optimization (PSO) variants based on five well-known benchmark functions in the area. According to the PSO's cognitive and social factors' contribution, we categorize the PSO algorithm into five variants. Different from other research work, which included only four PSO models, we propose an extra PSO variant called selfless Full-Model. Therefore, the five PSO variants, which named PSO Full-Model, PSO Cognitive-Only Model, PSO Social-Only Model, PSO Selfless Model and PSO Selfless Full-model, respectively, are applied to solve the benchmark functions. Their performances are compared based on the success rate, average function evaluations and the best fitness.

Fangyang Shen

Papers

FPGA based spike-time dependent encoder and reservoir design in neuromorphic computing processors

Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons

Low power CMOS power amplifier design for RFID and the Internet of Things

Modeling Differential Through-Silicon-Vias (TSVs) with Voltage Dependent and Nonlinear Capacitance

Performance Comparison of Partical Swarm Optimization Variant Models