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memristors, spintronic memories, and transistors, and can be trained using a range of software-based approaches, including error backpropagation and canonical learning rules. The field of neuromorphic engineering seeks to understand how the design and structure of artificial neural systems affects their computation, representation of information, adaptability, and overall function, with the ultimate aim of creating systems that exhibit similar properties to those found in nature. Wetware computers, which are composed of living neurons, are a conceptual form of neuromorphic computing that has been explored in limited prototypes.
796:
ranging from patterns that stabilize into homogeneity to those that become extremely complex and potentially Turing-complete. Amorphous computing refers to the study of computational systems using large numbers of parallel processors with limited computational ability and local interactions, regardless of the physical substrate. Examples of naturally occurring amorphous computation can be found in developmental biology, molecular biology, neural networks, and chemical engineering. The goal of amorphous computation is to understand and engineer novel systems through the characterization of amorphous algorithms as abstractions.
737:
including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, and more, which can be implemented using traditional electronic hardware or alternative physical media such as biomolecules or trapped-ion quantum computing devices. It also includes the study of understanding biological systems through engineering semi-synthetic organisms and viewing natural processes as information processing. The concept of the universe itself as a computational mechanism has also been proposed.
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membranes, and the communication between compartments and with the external environment plays a critical role in the computation. P systems are hierarchical and can be represented graphically, with rules governing the production, consumption, and movement of objects within and between regions. While these systems have largely remained theoretical, some have been shown to have the potential to solve NP-complete problems and have been proposed as hardware implementations for unconventional computing.
326:
made up of individual non-linear units that are connected in recurrent loops, allowing it to store information. Training is performed only at the readout stage, as the reservoir dynamics are fixed, and this framework allows for the use of naturally available systems, both classical and quantum mechanical, to reduce the effective computational cost. One key benefit of reservoir computing is that it allows for a simple and fast learning algorithm, as well as hardware implementation through
455:
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tangible user interfaces include the coupling of physical representations to underlying digital information and the embodiment of mechanisms for interactive control. There are five defining properties of tangible user interfaces, including the ability to multiplex both input and output in space, concurrent access and manipulation of interface components, strong specific devices, spatially aware computational devices, and spatial reconfigurability of devices.
838:. Ternary computers use trits, or ternary digits, which can be defined in several ways, including unbalanced ternary, fractional unbalanced ternary, balanced ternary, and unknown-state logic. Ternary quantum computers use qutrits instead of trits. Ternary computing has largely been replaced by binary computers, but it has been proposed for use in high-speed, low-power consumption devices using the Josephson junction as a balanced ternary memory cell.
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potential to solve certain computational problems, such as integer factorization, significantly faster than classical computers. However, there are several challenges to building practical quantum computers, including the difficulty of maintaining qubits' quantum states and the need for error correction. Quantum complexity theory is the study of the computational complexity of problems with respect to quantum computers.
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where the precision of the computation increases as the bit stream is extended. Stochastic computing can be used in iterative systems to achieve faster convergence, but it can also be costly due to the need for random bit stream generation and is vulnerable to failure if the assumption of independent bit streams is not met. It is also limited in its ability to perform certain digital functions.
612:
and several components that interact with their surroundings, such as sensors. MEMS and NEMS technology differ from molecular nanotechnology or molecular electronics in that they also consider factors such as surface chemistry and the effects of ambient electromagnetism and fluid dynamics. Applications of these technologies include accelerometers and sensors for detecting chemical substances.
463:
can consume a significant amount of energy in the process of converting electronic energy to photons and back. All-optical computers aim to eliminate the need for these conversions, leading to reduced electrical power consumption. Applications of optical computing include synthetic-aperture radar and optical correlators, which can be used for object detection, tracking, and classification.
350:
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and introducing small random changes to create a new generation. The population of solutions is subjected to natural or artificial selection and mutation, resulting in evolution towards increased fitness according to the chosen fitness function. Evolutionary computation has proven effective in various problem settings and has applications in both computer science and evolutionary biology.
882:
Stochastic computing is a method of computation that represents continuous values as streams of random bits and performs complex operations using simple bit-wise operations on the streams. It can be viewed as a hybrid analog/digital computer and is characterized by its progressive precision property,
810:
Evolutionary computation is a type of artificial intelligence and soft computing that uses algorithms inspired by biological evolution to find optimized solutions to a wide range of problems. It involves generating an initial set of candidate solutions, stochastically removing less desired solutions,
303:
A billiard-ball computer is a type of mechanical computer that uses the motion of spherical billiard balls to perform computations. In this model, the wires of a
Boolean circuit are represented by paths for the balls to travel on, the presence or absence of a ball on a path encodes the signal on that
651:
Molecular computing is an unconventional form of computing that utilizes chemical reactions to perform computations. Data is represented by variations in chemical concentrations, and the goal of this type of computing is to use the smallest stable structures, such as single molecules, as electronic
611:
Microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS) are technologies that involve the use of microscopic devices with moving parts, ranging in size from micrometers to nanometers. These devices typically consist of a central processing unit (such as an integrated circuit)
477:
Spintronics is a field of study that involves the use of the intrinsic spin and magnetic moment of electrons in solid-state devices. It differs from traditional electronics in that it exploits the spin of electrons as an additional degree of freedom, which has potential applications in data storage
94:
A model of computation describes how the output of a mathematical function is computed given its input. The model describes how units of computations, memories, and communications are organized. The computational complexity of an algorithm can be measured given a model of computation. Using a model
686:
DNA computing is a branch of unconventional computing that uses DNA and molecular biology hardware to perform calculations. It is a form of parallel computing that can solve certain specialized problems faster and more efficiently than traditional electronic computers. While DNA computing does not
592:
Superconducting computing is a form of cryogenic computing that utilizes the unique properties of superconductors, including zero resistance wires and ultrafast switching, to encode, process, and transport data using single flux quanta. It is often used in quantum computing and requires cooling to
534:
and entanglement, to perform calculations. Quantum computers use qubits, which are analogous to classical bits but can exist in multiple states simultaneously, to perform operations. While current quantum computers may not yet outperform classical computers in practical applications, they have the
515:
Fluidics, or fluidic logic, is the use of fluid dynamics to perform analog or digital operations in environments where electronics may be unreliable, such as those exposed to high levels of electromagnetic interference or ionizing radiation. Fluidic devices operate without moving parts and can use
492:
Atomtronics is a form of computing that involves the use of ultra-cold atoms in coherent matter-wave circuits, which can have components similar to those found in electronic or optical systems. These circuits have potential applications in several fields, including fundamental physics research and
462:
Optical computing is a type of computing that uses light waves, often produced by lasers or incoherent sources, for data processing, storage, and communication. While this technology has the potential to offer higher bandwidth than traditional computers, which use electrons, optoelectronic devices
795:
Cellular automata are discrete models of computation consisting of a grid of cells in a finite number of states, such as on and off. The state of each cell is determined by a fixed rule based on the states of the cell and its neighbors. There are four primary classifications of cellular automata,
379:
The term "human computer" refers to individuals who perform mathematical calculations manually, often working in teams and following fixed rules. In the past, teams of people were employed to perform long and tedious calculations, and the work was divided to be completed in parallel. The term has
325:
Reservoir computing is a computational framework derived from recurrent neural network theory that involves mapping input signals into higher-dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. The reservoir, which can be virtual or physical, is
214:
in nature. Analog computers were widely used in scientific and industrial applications, and were often faster than digital computers at the time. However, they started to become obsolete in the 1950s and 1960s and are now mostly used in specific applications such as aircraft flight simulators and
852:
Reversible computing is a type of unconventional computing where the computational process can be reversed to some extent. In order for a computation to be reversible, the relation between states and their successors must be one-to-one, and the process must not result in an increase in physical
755:
Neuromorphic computing involves using electronic circuits to mimic the neurobiological architectures found in the human nervous system, with the goal of creating artificial neural systems that are inspired by biological ones. These systems can be implemented using a variety of hardware, such as
671:
Peptide computing is a computational model that uses peptides and antibodies to solve NP-complete problems and has been shown to be computationally universal. It offers advantages over DNA computing, such as a larger number of building blocks and more flexible interactions, but has not yet been
434:
is a field of study that focuses on the coordination and control of multiple robots as a system. Inspired by the emergent behavior observed in social insects, swarm robotics involves the use of relatively simple individual rules to produce complex group behaviors through local communication and
364:
Tangible computing refers to the use of physical objects as user interfaces for interacting with digital information. This approach aims to take advantage of the human ability to grasp and manipulate physical objects in order to facilitate collaboration, learning, and design. Characteristics of
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Chaos computing is a type of unconventional computing that utilizes chaotic systems to perform computation. Chaotic systems can be used to create logic gates and can be rapidly switched between different patterns, making them useful for fault-tolerant applications and parallel computing. Chaos
736:
Biological computing, also known as bio-inspired computing or natural computation, is the study of using models inspired by biology to solve computer science problems, particularly in the fields of artificial intelligence and machine learning. It encompasses a range of computational paradigms
713:
Membrane computing, also known as P systems, is a subfield of computer science that studies distributed and parallel computing models based on the structure and function of biological membranes. In these systems, objects such as symbols or strings are processed within compartments defined by
307:
A domino computer is a mechanical computer that uses standing dominoes to represent the amplification or logic gating of digital signals. These constructs can be used to demonstrate digital concepts and can even be used to build simple information processing modules.
652:
components. This field, also known as chemical computing or reaction-diffusion computing, is distinct from the related fields of conductive polymers and organic electronics, which use molecules to affect the bulk properties of materials.
413:, or collaborative robots, are designed for direct interaction with humans within shared spaces and can be used for a variety of tasks, including information provision, logistics, and unergonomic tasks in industrial environments.
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Eshraghian, Jason K.; Ward, Max; Neftci, Emre; Wang, Xinxin; Lenz, Gregor; Dwivedi, Girish; Bennamoun, Mohammed; Jeong, Doo Seok; Lu, Wei D. (1 October 2021). "Training
Spiking Neural Networks Using Lessons from Deep Learning".
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interaction with the environment. This approach is characterized by the use of large numbers of simple robots and promotes scalability through the use of local communication methods such as radio frequency or infrared.
258:, which have dominated computer science for more than half a century". These methods model their computational operations based on non-standard paradigms, and are currently mostly in the research and development stage.
566:. They both construct a system (a circuit) that represents the physical problem at hand, and then leverage their respective physics properties of the system to seek the "minimum". Neuromorphic quantum computing and
691:, it can perform a high number of parallel computations simultaneously. However, DNA computing has slower processing speeds, and it is more difficult to analyze the results compared to digital computers.
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entropy. Quantum circuits are reversible as long as they do not collapse quantum states, and reversible functions are bijective, meaning they have the same number of inputs as outputs.
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van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto (April 2017).
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Broughton, Michael; Verdon, Guillaume; McCourt, Trevor; Martinez, Antonio J.; Yoo, Jae Hyeon; Isakov, Sergei V.; Massey, Philip; Halavati, Ramin; Niu, Murphy
Yuezhen (2021-08-26),
250:
Unconventional computing is, according to a conference description, "an interdisciplinary research area with the main goal to enrich or go beyond the standard models, such as the
530:
Quantum computing, perhaps the most well-known and developed unconventional computing method, is a type of computation that utilizes the principles of quantum mechanics, such as
1410:
1613 'R. B.' Yong Mans
Gleanings 1, I have read the truest computer of Times, and the best Arithmetician that ever breathed, and he reduceth thy dayes into a short number.
163:
Mechanical computers retain some interest today, both in research and as analogue computers. Some mechanical computers have a theoretical or didactic relevance, such as
98:
A wide variety of models are commonly used; some closely resemble the workings of (idealized) conventional computers, while others do not. Some commonly used models are
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Tanaka, Gouhei; Yamane, Toshiyuki; Héroux, Jean Benoit; Nakane, Ryosho; Kanazawa, Naoki; Takeda, Seiji; Numata, Hidetoshi; Nakano, Daiju; Hirose, Akira (2019-07-01).
409:, or HRI, is the study of interactions between humans and robots. It involves contributions from fields such as artificial intelligence, robotics, and psychology.
573:
1684:; Miniatura, C.; Kwek, L.-C.; Aghamalyan, D.; Ahufinger, V.; Anderson, D.; Andrei, N.; Arnold, A. S.; Baker, M.; Bell, T. A.; Bland, T.; Brantut, J. P. (2021).
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While some are actually simulated, others are not. No attempt is made to build a functioning computer through the mechanical collisions of billiard balls. The
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and transfer, as well as quantum and neuromorphic computing. Spintronic systems are often created using dilute magnetic semiconductors and
Heusler alloys.
1032:
95:
allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.
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Both billiard-ball computers and domino computers are examples of unconventional computing methods that use physical objects to perform computation.
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1522:
Kim, S. K.; Goda, K.; Fard, A. M.; Jalali, B. (2011). "Optical time-domain analog pattern correlator for high-speed real-time image recognition".
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nonlinear amplification, similar to transistors in electronic digital logic. Fluidics are also used in nanotechnology and military applications.
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based on digital electronics, with extensive integration made possible following the invention of the transistor and the scaling of
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also been used more recently to describe individuals with exceptional mental arithmetic skills, also known as mental calculators.
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and neuromorphic quantum computing are physics-based unconventional computing approaches to computations and don't follow the
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17:
1753:; von Klitzing, Wolf (2022-06-14). "Colloquium : Atomtronic circuits: From many-body physics to quantum technologies".
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Torlai, Giacomo; Mazzola, Guglielmo; Carrasquilla, Juan; Troyer, Matthias; Melko, Roger; Carleo, Giuseppe (2018-02-26).
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Kim, Mi Jeong; Maher, Mary Lou (30 May 2008). "The Impact of
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Neuromorphic
Quantum Computing (abbreviated as 'n.quantum computing') is an unconventional type of computing that uses
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1914:
231:, a mechanical integrator for calculating the area of an arbitrary 2D shape, are also examples of analog computing.
2929:
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M. Sakthi Balan; Kamala
Krithivasan; Y. Sivasubramanyam (2002). "Peptide Computing - Universality and Complexity".
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Maan, A. K.; Jayadevi, D. A.; James, A. P. (2016-01-01). "A Survey of
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Wolf, S. A.; Chtchelkanova, A. Y.; Treger, D. M. (2006). "Spintronics—A retrospective and perspective".
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These are unintuitive and pedagogical examples that a computer can be made out of almost anything.
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Neuromorphic
Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling
2131:"Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems"
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and used at the First International Conference on Unconventional Models of Computation in 1998.
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allows for a variety of methods of computation. Computing technology was first developed using
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Proceedings of the 2nd international conference on Tangible and embedded interaction - TEI '08
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This computing behavior can be "simulated" using classical silicon-based micro-transistors or
202:, which are continuous physical quantities, to model and solve problems. These signals can be
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wire, and gates are simulated by collisions of balls at points where their paths intersect.
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Amico, Luigi; Anderson, Dana; Boshier, Malcolm; Brantut, Jean-Philippe; Kwek, Leong-Chuan;
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computing has been applied to various fields such as meteorology, physiology, and finance.
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G.Rozenberg, T.Back, J.Kok, Editors, Handbook of Natural Computing, Springer Verlag, 2012
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practically realized due to the limited availability of specific monoclonal antibodies.
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teaching control systems in universities. Examples of analog computing devices include
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Sharir, Or; Levine, Yoav; Wies, Noam; Carleo, Giuseppe; Shashua, Amnon (2020-01-16).
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227:, a mechanical device that calculates the positions of planets and the Moon, and the
2847:"Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch"
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2260:"Superconducting quantum many-body circuits for quantum simulation and computing"
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944:"Unconventional Computing: A Brief Subjective History, CDMTCS Report 480, 2015"
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systems and then evolved into the use of electronic devices. Other fields of
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2801:
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1906:
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the development of practical devices such as sensors and quantum computers.
2722:
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555:, can be computed equally efficiently with neuromorphic quantum computing.
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Realization of a photonic controlled-NOT gate for use in quantum computing
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223:, and complex mechanisms for process control and protective relays. The
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2007:"Solving the quantum many-body problem with artificial neural networks"
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1991:
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is another theoretically interesting mechanical computing scheme.
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to perform quantum operations. It was suggested that
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830:Ternary computing is a type of computing that uses
196:An analog computer is a type of computer that uses
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2952:
2746:"Mott Memory and Neuromorphic Devices"
2234:Di Ventra, Massimiliano (2022-03-23),
2210:Di Ventra, Massimiliano (2022-03-23),
1805:
1799:
1636:
1157:
956:
632:
593:cryogenic temperatures for operation.
314:
2559:A.Brabazon, M.O'Neill, S.McGarraghy.
1899:Nano, Quantum and Molecular Computing
1469:
1427:(2015-11-15). Retrieved on 2016-01-19
1327:
1135:, Springer-Verlag, pp. 135–160,
694:
438:
334:
268:
2508:"Introduction to Membrane Computing"
2320:Nanocomputers and Swarm Intelligence
1932:"The Problem with Quantum Computers"
1197:, a detailed description written by
1021:
819:
660:
519:
443:
1680:Amico, L.; Boshier, M.; Birkl, G.;
1165:"Domino computer - Everything2.com"
276:
185:
27:Computing by new or unusual methods
24:
2859:
2784:
1476:. Simon and Schuster. p. 34.
893:Network computing (disambiguation)
856:
416:
368:
25:
2971:
2926:Explorations in Quantum Computing
1275:Journal of Physics Communications
252:Von Neumann computer architecture
2505:
1637:Bhatti, S.; et al. (2017).
675:
511:A flip flop made using fluidics.
167:, while hydraulic ones like the
2884:
2839:
2817:
2737:
2613:
2566:
2531:
2520:from the original on 2011-07-22
2499:
2485:10.1093/bioinformatics/17.4.364
2457:
2422:
2308:
2251:
2227:
2203:
2179:
2122:
2063:
1998:
1974:
1942:
1923:
1890:
1832:
1742:
1673:
1618:
1593:
1566:
1515:
1490:
1463:
1430:
1415:
1397:
1321:
1262:
1203:
740:
724:Biologically-inspired computing
2165:10.1103/PhysRevLett.124.020503
1983:Neuromorphic quantum computing
1120:
1074:(1982), "Conservative logic",
1060:
1046:
950:
935:
921:
628:, useful as a molecular switch
624:Graphical representation of a
603:Microelectromechanical systems
597:Microelectromechanical systems
539:Neuromorphic quantum computing
481:
466:
13:
1:
1852:(10th anniversary ed.).
1810:. Cham: Springer. p. 3.
914:
709:Nine Region Membrane Computer
607:Nanoelectromechanical systems
63:
2561:Natural Computing Algorithms
1954:CORDIS | European Commission
1785:10.1103/RevModPhys.94.041001
1656:10.1016/j.mattod.2017.07.007
1239:10.1016/j.neunet.2019.03.005
1031:. 2014-03-18. Archived from
788:" in the cellular automaton
358:Children's Creativity Museum
235:Electronic digital computers
7:
1141:10.1007/978-1-4471-0129-1_6
886:
639:Molecular scale electronics
496:
10:
2976:
2924:Colin P. Williams (2011).
2762:10.1109/JPROC.2015.2431914
2707:10.1109/TNNLS.2016.2547842
2591:10.1038/s41928-021-00646-1
2539:U.S. patent 20,090,124,506
1371:Human–Computer Interaction
1007:. OUP Oxford. p. 90.
1001:Johnston, Sean F. (2006).
875:
860:
845:
823:
803:
763:
744:
721:
698:
679:
664:
636:
600:
585:
523:
500:
485:
470:
447:
420:
387:
372:
338:
318:
280:
189:
137:
87:
50:unconventional computation
2899:, Penguin Books, London,
2354:; Evoy, Stephane (2004).
2108:10.1038/s41567-018-0048-5
1755:Reviews of Modern Physics
1456:10.4249/scholarpedia.1463
1406:Oxford English Dictionary
1383:10.1080/07370020802016415
1133:Collision-Based Computing
588:Superconducting computing
582:Superconducting computing
2441:10.1007/3-540-48017-X_27
1858:10.1017/CBO9780511976667
1306:10.1088/2399-6528/aad56d
1193:August 16, 2006, at the
806:Evolutionary computation
800:Evolutionary computation
564:von Neumann architecture
402:Human-robot interaction.
241:Von Neumann architecture
31:Unconventional computing
2896:Darwin's Dangerous Idea
2802:10.1145/3407197.3407204
2750:Proceedings of the IEEE
2563:, Springer Verlag, 2015
2352:Ventra, Massimiliano Di
2264:Applied Physics Letters
2135:Physical Review Letters
2041:10.1126/science.aag2302
1907:10.1007/1-4020-8068-9_8
1338:10.1145/1347390.1347392
815:Mathematical approaches
656:Biochemistry approaches
407:Human-robot interaction
390:Human–robot interaction
384:Human-robot interaction
345:Tangible user interface
175:were used effectively.
165:billiard-ball computers
39:nonstandard computation
2350:Hughes, James E. Jr.;
792:
747:Neuromorphic computing
710:
629:
578:
545:neuromorphic computing
512:
459:
403:
361:
300:
283:billiard-ball computer
149:
104:random-access machines
68:The general theory of
2327:John Wiley & Sons
1806:Hidary, Jack (2019).
790:Conway's Game of Life
777:
718:Biological approaches
708:
623:
576:
510:
457:
401:
352:
294:
225:Antikythera mechanism
147:
84:Models of Computation
35:alternative computing
18:Alternative computing
2960:Classes of computers
2360:. Berlin: Springer.
1901:. pp. 247–266.
1544:10.1364/ol.36.000220
1470:Nolte, D.D. (2001).
878:Stochastic computing
872:Stochastic computing
848:Reversible computing
842:Reversible computing
732:Biological computing
689:computability theory
647:Molecular logic gate
616:Chemistry approaches
154:mechanical computers
134:Mechanical computing
90:Model of computation
2699:2016arXiv160407121M
2638:2017NatMa..16..414V
2286:2020ApPhL.116w0501W
2157:2020PhRvL.124b0503S
2100:2018NatPh..14..447T
2033:2017Sci...355..602C
1936:Scientific American
1777:2022RvMP...94d1001A
1712:2021AVSQS...3c9201A
1690:AVS Quantum Science
1587:10.1147/rd.501.0101
1536:2011OptL...36..220K
1297:2018JPhCo...2h5007R
1090:1982IJTP...21..219F
770:Amorphous computing
633:Molecular computing
577:A quantum computer.
553:quantum computation
328:physical reservoirs
321:Reservoir computing
315:Reservoir computing
299:built from dominoes
140:Mechanical computer
2932:. pp. 25–29.
2579:Nature Electronics
1098:10.1007/BF01857727
964:. Addison-Wesley.
905:hydraulic computer
898:WDR paper computer
793:
711:
701:membrane computing
695:Membrane computing
643:Chemical computing
630:
579:
549:quantum algorithms
513:
460:
439:Physics approaches
427:swarm intelligence
404:
362:
335:Tangible computing
301:
269:Generic approaches
150:
54:Cristian S. Calude
2939:978-1-84628-887-6
2905:978-0-14-016734-4
2683:(99): 1734–1746.
2450:978-3-540-43775-8
2367:978-1-4020-7720-3
2294:10.1063/5.0008202
2017:(6325): 602–606.
1867:978-0-511-99277-3
1817:978-3-030-23922-0
1720:10.1116/5.0026178
1508:978-0-262-06112-4
1483:978-0-7432-0501-6
1347:978-1-60558-004-3
1150:978-1-4471-0129-1
1129:Adamatzky, Andrew
826:Ternary computing
820:Ternary computing
766:Cellular automata
728:natural computing
667:peptide computing
661:Peptide computing
568:quantum computing
560:quantum computing
558:Both traditional
526:Quantum computing
520:Quantum computing
450:Optical computing
444:Optical computing
124:cellular automata
116:rewriting systems
100:register machines
16:(Redirected from
2967:
2944:
2943:
2921:
2915:
2888:
2882:
2881:
2879:
2878:
2863:
2857:
2856:
2855:. 31 March 2020.
2843:
2837:
2836:
2834:
2821:
2815:
2814:
2804:
2788:
2782:
2781:
2756:(8): 1289–1310.
2741:
2735:
2734:
2692:
2672:
2666:
2665:
2646:10.1038/nmat4856
2626:Nature Materials
2617:
2611:
2610:
2570:
2564:
2557:
2551:
2548:
2542:
2541:
2535:
2529:
2528:
2526:
2525:
2519:
2512:
2506:Păun, Gheorghe.
2503:
2497:
2496:
2461:
2455:
2454:
2426:
2420:
2419:
2417:
2416:
2410:
2404:. Archived from
2387:
2378:
2372:
2371:
2347:
2341:
2340:
2312:
2306:
2305:
2279:
2255:
2249:
2248:
2247:
2231:
2225:
2224:
2223:
2207:
2201:
2200:
2199:
2183:
2177:
2176:
2150:
2126:
2120:
2119:
2093:
2067:
2061:
2060:
2026:
2002:
1996:
1995:
1994:
1978:
1972:
1971:
1969:
1968:
1946:
1940:
1939:
1927:
1921:
1920:
1894:
1888:
1887:
1840:Nielsen, Michael
1836:
1830:
1829:
1803:
1797:
1796:
1770:
1746:
1740:
1739:
1705:
1677:
1671:
1670:
1668:
1658:
1634:
1628:
1622:
1616:
1615:
1613:
1612:
1603:. Archived from
1597:
1591:
1590:
1570:
1564:
1563:
1519:
1513:
1512:
1494:
1488:
1487:
1467:
1461:
1460:
1458:
1439:"Swarm Robotics"
1434:
1428:
1425:The Manufacturer
1419:
1413:
1412:
1401:
1395:
1394:
1366:
1360:
1359:
1325:
1319:
1318:
1308:
1290:
1266:
1260:
1259:
1241:
1231:
1207:
1201:
1188:Domino computers
1185:
1179:
1178:
1176:
1175:
1161:
1155:
1153:
1124:
1118:
1116:
1084:(3–4): 219–253,
1072:Toffoli, Tommaso
1064:
1058:
1057:
1050:
1044:
1043:
1041:
1040:
1025:
1019:
1018:
998:
992:
982:
976:
975:
954:
948:
947:
939:
933:
932:
925:
909:Hypercomputation
751:wetware computer
360:in San Francisco
277:Physical objects
186:Analog computing
173:Water integrator
120:digital circuits
21:
2975:
2974:
2970:
2969:
2968:
2966:
2965:
2964:
2950:
2949:
2948:
2947:
2940:
2922:
2918:
2889:
2885:
2876:
2874:
2866:Sincell, Mark.
2864:
2860:
2845:
2844:
2840:
2822:
2818:
2789:
2785:
2742:
2738:
2673:
2669:
2618:
2614:
2571:
2567:
2558:
2554:
2549:
2545:
2537:
2536:
2532:
2523:
2521:
2517:
2510:
2504:
2500:
2462:
2458:
2451:
2427:
2423:
2414:
2412:
2408:
2385:
2379:
2375:
2368:
2348:
2344:
2337:
2329:. p. 205.
2313:
2309:
2256:
2252:
2232:
2228:
2208:
2204:
2184:
2180:
2127:
2123:
2068:
2064:
2003:
1999:
1979:
1975:
1966:
1964:
1948:
1947:
1943:
1928:
1924:
1917:
1895:
1891:
1868:
1837:
1833:
1818:
1804:
1800:
1747:
1743:
1678:
1674:
1643:Materials Today
1635:
1631:
1623:
1619:
1610:
1608:
1599:
1598:
1594:
1571:
1567:
1520:
1516:
1509:
1495:
1491:
1484:
1468:
1464:
1435:
1431:
1420:
1416:
1403:
1402:
1398:
1367:
1363:
1348:
1332:. pp. xv.
1326:
1322:
1267:
1263:
1216:Neural Networks
1208:
1204:
1195:Wayback Machine
1186:
1182:
1173:
1171:
1169:everything2.com
1163:
1162:
1158:
1151:
1125:
1121:
1068:Fredkin, Edward
1065:
1061:
1052:
1051:
1047:
1038:
1036:
1027:
1026:
1022:
1015:
999:
995:
983:
979:
972:
958:Savage, John E.
955:
951:
940:
936:
927:
926:
922:
917:
889:
880:
874:
865:
863:Chaos computing
859:
857:Chaos computing
850:
844:
828:
822:
817:
808:
802:
772:
764:Main articles:
762:
753:
745:Main articles:
743:
734:
722:Main articles:
720:
703:
697:
684:
678:
669:
663:
658:
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637:Main articles:
635:
618:
609:
601:Main articles:
599:
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584:
541:
528:
522:
505:
499:
490:
484:
475:
469:
452:
446:
441:
429:
421:Main articles:
419:
417:Swarm computing
396:
388:Main articles:
386:
377:
371:
369:Human computing
347:
339:Main articles:
337:
331:
323:
317:
289:
287:domino computer
281:Main articles:
279:
271:
237:
194:
192:analog computer
188:
180:domino computer
142:
136:
112:lambda calculus
108:Turing machines
92:
86:
66:
33:(also known as
28:
23:
22:
15:
12:
11:
5:
2973:
2963:
2962:
2946:
2945:
2938:
2916:
2891:Daniel Dennett
2883:
2858:
2838:
2816:
2783:
2736:
2667:
2632:(4): 414–418.
2612:
2585:(9): 635–644.
2565:
2552:
2543:
2530:
2498:
2479:(4): 364–368.
2473:Bioinformatics
2469:Rainer Schuler
2456:
2449:
2421:
2373:
2366:
2342:
2335:
2307:
2250:
2226:
2202:
2178:
2121:
2084:(5): 447–450.
2077:Nature Physics
2062:
1997:
1973:
1962:10.3030/828826
1941:
1922:
1915:
1889:
1866:
1831:
1816:
1798:
1751:Minguzzi, Anna
1741:
1672:
1649:(9): 530–548.
1629:
1617:
1592:
1565:
1524:Optics Letters
1514:
1507:
1489:
1482:
1462:
1429:
1414:
1396:
1377:(2): 101–137.
1361:
1346:
1320:
1261:
1202:
1199:David Johnston
1180:
1156:
1149:
1119:
1059:
1045:
1020:
1014:978-0191513886
1013:
993:
985:Penrose, Roger
977:
971:978-0201895391
970:
949:
934:
919:
918:
916:
913:
912:
911:
906:
900:
895:
888:
885:
876:Main article:
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861:Main article:
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846:Main article:
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804:Main article:
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586:Main article:
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486:Main article:
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471:Main article:
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448:Main article:
445:
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432:Swarm robotics
423:Swarm robotics
418:
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385:
382:
375:Human computer
373:Main article:
370:
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336:
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319:Main article:
316:
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278:
275:
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256:Turing machine
236:
233:
199:analog signals
190:Main article:
187:
184:
152:Historically,
138:Main article:
135:
132:
88:Main article:
85:
82:
78:modern physics
65:
62:
52:was coined by
26:
9:
6:
4:
3:
2:
2972:
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2913:0-14-016734-X
2910:
2906:
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2898:
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2892:
2887:
2873:
2869:
2868:"Future Tech"
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2434:
2433:
2432:DNA Computing
2425:
2411:on 2015-06-15
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2403:
2399:
2395:
2391:
2384:
2377:
2369:
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2359:
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2336:9781848210097
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1859:
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1850:
1845:
1844:Chuang, Isaac
1841:
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1819:
1813:
1809:
1802:
1794:
1790:
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1761:(4): 041001.
1760:
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1696:(3): 039201.
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1648:
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1607:on 2011-04-18
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