Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem

Saito, Kenta; Aono, Masashi; Kasai, Seiya

doi:10.1038/s41598-020-77617-7

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…The latter approach to neuromorphic devices has long been a major trend based on chaotic dynamics included in nonlinear physics, and is known as the neuro-computer [20,21]. Also, amoeba was used as a key element for bio-inspired information processing [22,23]. Furthermore, over the recent decade, biomimetic signal/information processing material-based devices has been of attraction.…”

Section: Information Processing In Biological Systems Using Noise 21 ...mentioning

confidence: 99%

Stochastic Resonance in Organic Electronic Devices

Suzuki

Asakawa

2022

Polymers

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Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.

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Section: Information Processing In Biological Systems Using Noise 21 ...mentioning

confidence: 99%

Stochastic Resonance in Organic Electronic Devices

Suzuki

Asakawa

2022

Polymers

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“…This approach was theoretically further developed, including explicitly extending to the case of solving Ising problems and analyzing various possible physical implementations [93][94][95][96][97][98] . Oscillator networks have been experimentally demonstrated with various systems such as bulk analog electronic 95,[99][100][101] , VO 2 insulator-to-metal transition 31,102,103 (Fig. 2c), spin 104,105 and integrated CMOS electronic [106][107][108] oscillators to solve problems such as graph coloring, maximum independent set, and the Ising model.…”

Section: Oscillator-based Computingmentioning

confidence: 99%

Ising machines as hardware solvers of combinatorial optimization problems

Mohseni¹,

McMahon²,

Byrnes³

2022

Preprint

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Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications for a wide variety of optimization problems. In this review, we survey the current status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as spintronics, optics, memristors, and digital hardware accelerators; dynamical-systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and discuss their strengths and weaknesses. Key points• Dedicated hardware solvers for the Ising model are of great interest due to the many potential practical applications and the end of Moore's law which motivate alternative computational approaches.• Three main computing methods that Ising machines employ are classical annealing, quantum annealing, and dynamical system evolution. A single machine can operate based on multiple computing approaches.• Today, Ising hardware based on classical digital technologies are the best performing for common benchmark problems. However, the performance is problem dependent and alternative methods can perform well for particular classes of problems.• For particular crafted problem instances, quantum approaches have been observed to have a superior performance over classical algorithms, motivating quantum hardware approaches and quantum-inspired classical algorithms.• Hybrid quantum-classical and digital-analog algorithms are promising for future development; they may harness the complementary advantages of both.

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Brain and Its Universal Logical Model of Multi-Agent Biological Systems

2022

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We build a topological model, based on intuitionistic logic, for multi-agent biological systems (such as Physarum polycephalum, bacterial colonies or any other swarm), reacting to external nourishment stimuli. Our construction follows the topological description of brain activity, where particles (neurons) are activated by an external environment, represented by a topological space X with an open cover $$\{U_i:i\in I\}$$ { U i : i ∈ I } . The brain builds the model of this external space via the nerve (trace) of a topological space X. Here the body of Physarum polycephalum or a swarm made of networks of tubular structures represents a nerve (trace) of X also which means that Physarum polycephalum or a swarm gains orientation in the space of external stimuli even in the absence of any neural system. The logic of living organisms is based on open subsets of X and thus can be represented by Heyting algebra (i.e. intuitionistically). We also consider the generalisation of the nerve construction to a categorical context, where the category is determined by the network structures of multi-agent biological system. This model can be generalised up to simulating the behaviour of any swarm by means of intuitionistic logic.

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Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem

Cited by 10 publications

References 45 publications

Stochastic Resonance in Organic Electronic Devices

Stochastic Resonance in Organic Electronic Devices

Ising machines as hardware solvers of combinatorial optimization problems

Brain and Its Universal Logical Model of Multi-Agent Biological Systems

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