1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

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.

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

The Role of Advanced Sensing in Smart Cities

Figures of merit connecting processing capabilities with power dissipated (OpS/Watt, Joule/bit, etc.) are becoming dominant factors in choosing technologies for implementing the next generation of computing and communication network systems. Superconductivity is viewed as a technology capable of achieving higher energy efficiencies than other technologies. Static power dissipation of standard RSFQ logic, associated with dc bias resistors, is responsible for most of the circuit power dissipation. In this paper, we review and compare different superconductor digital technology approaches and logic families addressing this problem. We present a novel energy-efficient single flux quantum logic family, ERSFQ/eSFQ. We also discuss energy-efficient approaches for output data interface and overall cryosystem design.

/pdf/energy-efficient-single-flux-quantum-technology-144o6vu2zy.pdf

Energy-Efficient Single Flux Quantum Technology

This paper presents a low power boost converter for thermoelectric energy harvesting that demonstrates an efficiency that is 15% higher than the state-of-the-art for voltage conversion ratios above 20. This is achieved by utilizing a technique allowing synchronous rectification in the discontinuous conduction mode. A low-power method for input voltage monitoring is presented. The low input voltage requirements allow operation from a thermoelectric generator powered by body heat. The converter, fabricated in a 0.13 μm CMOS process, operates from input voltages ranging from 20 mV to 250 mV while supplying a regulated 1 V output. The converter consumes 1.6 (1.1) μW of quiescent power, delivers up to 25 (175) μW of output power, and is 46 (75)% efficient for a 20 mV and 100 mV input, respectively.

A 20 mV Input Boost Converter With Efficient Digital Control for Thermoelectric Energy Harvesting

A thermosensing CMOS circuit that changes its internal voltage steeply at a critical temperature was developed. The circuit is based on a self-biasing circuit technique and uses the temperature-sensitive characteristics of MOSFETs operating in the subthreshold region. To develop this sensor device, a method to analyze self-biasing circuits, which is different from a conventional one, was employed. This method is useful for understanding the self-biasing circuit operation. A temperature sensor device makes use of a MOSFET resistor’s transition from a strong inversion to a weak-inversion or subthreshold operation. The temperature at which the transition occurs can be set to a desired value by adjusting the parameters of MOSFETs in the circuit. The sensor LSI can be made using a standard CMOS process and can be used as over-temperature and over-current protectors for LSI circuits.  2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

/pdf/a-highly-sensitive-thermosensing-cmos-circuit-based-on-self-vk32v4hx1y.pdf

A Highly Sensitive Thermosensing CMOS Circuit Based on Self-Biasing Circuit Technique

Characterization of deep interface states in SiO2/B-doped diamond using the transient photocapacitance method

A neural network exhibiting precisely timed synchronization in a noisy environment with depressing synapses was proposed by Fukai and Kanemura [1]. Based on this network, we constructed neural network hardware using silicon neurons and depressing synapse circuits and evaluated timing precision among the neurons using a simulation program with integrated circuit emphasis (SPICE). Consequently, timing jitter among the neurons was significantly reduced with depressing synapse circuits compared to nondepressing synapses. Moreover, a novel analog circuit mimicking characteristics of spike-timing dependent plasticity (STDP) was proposed to construct a neural network that exhibits robust synchronization in a noisy environment. We demonstrate the circuit’s basic learning characteristics using SPICE.

Neuromorphic MOS Circuits Exhibiting Precisely Timed Synchronization with Silicon Spiking Neurons and Depressing Synapses (Special Issue on Nonlinear Circuits and Signal Processing)

Temperature Compensated Nano-Ampere CMOS Current Reference Circuit Using Small Offset Voltage

Noise-induced synchronization is a phenomenon where populations of independent oscillators are synchronized by applying common noises to the oscillators without all-to-all coupling. Recently, Nakao et al. proposed a mathematical model of the noise-induced synchronization [1]. We utilize this model for the phase synchronization of on-chip multiple clock sources in digital LSIs. We apply common noises to all the clock sources in order to synchronize the clock sources, aiming at global clock distribution without wiring delay. We report that these oscillators can be synchronized even if each clock frequency generated by several clock sources have slight difference. We constructed a Wilson-Cowan oscillator circuit with 0.25 µm CMOS parameters, which operated at 1.17 GHz. Through circuit simulations, we demonstrate that identical oscillators can be synchronized by artificial noises. Then we explore the effect of mismatches among oscillator circuits, and demonstrate that phase differences among nonidentical oscillator circuits are suppressed by noises if the threshold voltage difference of a dominant MOSFET in the circuits is smaller than 12 mV.

Tetsuya Hirose

Papers

A Highly Sensitive Thermosensing CMOS Circuit Based on Self-Biasing Circuit Technique

Characterization of deep interface states in SiO2/B-doped diamond using the transient photocapacitance method

Neuromorphic MOS Circuits Exhibiting Precisely Timed Synchronization with Silicon Spiking Neurons and Depressing Synapses (Special Issue on Nonlinear Circuits and Signal Processing)

Temperature Compensated Nano-Ampere CMOS Current Reference Circuit Using Small Offset Voltage

Noise-Induced Phase Synchronization among Nonidentical Analog CMOS Oscillators