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)

https://users.dimi.uniud.it/~antonio.dangelo/Robotica/2012/helper/leggedRobot/Ijspeert08NN.pdf

2008 Special Issue: Central pattern generators for locomotion control in animals and robots: A review

The goal of this paper is to review in brief the basic physics of single-election devices, as well as their-current and prospective applications. These devices based on the controllable transfer of single electrons between small conducting "islands", have already enabled several important scientific experiments. Several other applications of analog single-election devices in unique scientific instrumentation and metrology seem quite feasible. On the other hand, the prospect of silicon transistors being replaced by single-electron devices in integrated digital circuits faces tough challenges and remains uncertain. Nevertheless, even if this replacement does not happen, single electronics will continue to play an important role by shedding light on the fundamental size limitations of new electronic devices. Moreover, recent research in this field has generated some by-product ideas which may revolutionize random-access-memory and digital-data-storage technologies.

Single-electron devices and their applications

The electronic properties of inorganic devices such as memristors can be used to simulate neurological behaviour. In particular, ionic and electronic effects in a silver sulphide device are now shown to mimic short- and long-term synaptic functions. Memory is believed to occur in the human brain as a result of two types of synaptic plasticity: short-term plasticity (STP) and long-term potentiation (LTP; refs 1, 2, 3, 4). In neuromorphic engineering5,6, emulation of known neural behaviour has proven to be difficult to implement in software because of the highly complex interconnected nature of thought processes. Here we report the discovery of a Ag2S inorganic synapse, which emulates the synaptic functions of both STP and LTP characteristics through the use of input pulse repetition time. The structure known as an atomic switch7,8, operating at critical voltages, stores information as STP with a spontaneous decay of conductance level in response to intermittent input stimuli, whereas frequent stimulation results in a transition to LTP. The Ag2S inorganic synapse has interesting characteristics with analogies to an individual biological synapse, and achieves dynamic memorization in a single device without the need of external preprogramming. A psychological model related to the process of memorizing and forgetting is also demonstrated using the inorganic synapses. Our Ag2S element indicates a breakthrough in mimicking synaptic behaviour essential for the further creation of artificial neural systems that emulate characteristics of human memory.

/pdf/short-term-plasticity-and-long-term-potentiation-mimicked-in-3c6onjbc0r.pdf

Short-term plasticity and long-term potentiation mimicked in single inorganic synapses

Computational geometry

A large oxide flow has been found to appear when a SiO2-GeO2 mixed glass film is treated in a low-pressure H2 gas at 900°C. Simultaneously, the greater part of the GeO2 component is evaporated. The flow mechanism is explained by the diffusion and evaporation of GeO molecules produced by GeO2 reduction. A planarization process utilizing this flow exhibits a large effect at low temperatures in comparison with phosphosilicate-glass flow. The oxide profile after a H2 treatment is no longer changed by a high-temperature treatment. The formed oxide is highly durable against acids. Electrical properties such as the dielectric constant, resistivity, and dielectric strength are comparable to those of a SiO2 film grown by the thermal oxidation of Si. The problem of a small amount of Ge crystallized on the bottom of the oxide layer is controlled by a decrease in the H2 pressure.

A Planarization Technique Utilizing Oxide Flow during H 2 Treatment of a SiO 2 -GeO 2 Film

We discuss the integration architecture of spiking neurons, predicted to be next-generation basic circuits of neural processor and dynamic synapse circuits. A key to development of a brain-like processor is to learn from the brain. Learning from the brain, we try to develop circuits implementing neuron and synapse functions while enabling large-scale integration, so large-scale integrated circuits (LSIs) realize functional behavior of neural networks. With such VLSI, we try to construct a large-scale neural network on a single semiconductor chip. With circuit integration now reaching micron levels, however, problems have arisen in dispersion of device performance in analog IC and in the influence of electromagnetic noise. A genuine brain computer should solve such problems on the network level rather than the element level. To achieve such a target, we must develop an architecture that learns brain functions sufficiently and works correctly even in a noisy environment. As the first step, we propose an analog circuit architecture of spiking neurons and dynamic synapses representing the model of artificial neurons and synapses in a form closer to that of the brain. With the proposed circuit, the model of neurons and synapses can be integrated on a silicon chip with metal-oxide-semiconductor (MOS) devices. In the sections that follow, we discuss the dynamic performance of the proposed circuit by using a circuit simulator, HSPICE. As examples of networks using these circuits, we introduce a competitive neural network and an active pattern recognition network by extracting firing frequency information from input information. We also show simulation results of the operation of networks constructed with the proposed circuits.

Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses

http://lalsie.ist.hokudai.ac.jp/publication/dlcenter.php?fn=paper/sm_2004_oya.pdf

Yoshihito Amemiya

Papers

A Planarization Technique Utilizing Oxide Flow during H 2 Treatment of a SiO 2 -GeO 2 Film

Basic Circuit Design of a Neural Processor: Analog CMOS Implementation of Spiking Neurons and Dynamic Synapses

A single-electron circuit as a discrete dynamical system

NEW PLANARIZATION TECHNIQUE FOR LSI FABRICATION UTILIZING Si-Ge FILM OXIDATION.

Elektrodenvorrichtung fuer eine halbleitervorrichtung