Search results for 'Analog' (try it on Scholar)

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  1. Matthew Katz (2008). Analog and Digital Representation. Minds and Machines 18 (3):403-408.score: 18.0
    In this paper, I argue for three claims. The first is that the difference between analog and digital representation lies in the format and not the medium of representation. The second is that whether a given system is analog or digital will sometimes depend on facts about the user of that system. The third is that the first two claims are implicit in Haugeland's (1998) account of the distinction.
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  2. Corey J. Maley (2011). Analog and Digital, Continuous and Discrete. Philosophical Studies 155 (1):117-131.score: 18.0
    Representation is central to contemporary theorizing about the mind/brain. But the nature of representation--both in the mind/brain and more generally--is a source of ongoing controversy. One way of categorizing representational types is to distinguish between the analog and the digital: the received view is that analog representations vary smoothly, while digital representations vary in a step-wise manner. I argue that this characterization is inadequate to account for the ways in which representation is used in cognitive science; in its (...)
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  3. Russell Trenholme (1994). Analog Simulation. Philosophy of Science 61 (1):115-131.score: 18.0
    The distinction between analog and digital representation is reexamined; it emerges that a more fundamental distinction is that between symbolic and analog simulation. Analog simulation is analyzed in terms of a (near) isomorphism of causal structures between a simulating and a simulated process. It is then argued that a core concept, naturalistic analog simulation, may play a role in a bottom-up theory of adaptive behavior which provides an alternative to representational analyses. The appendix discusses some formal (...)
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  4. Aldo Frigerio, Alessandro Giordani & Luca Mari (2013). On Representing Information: A Characterization of the Analog/Digital Distinction. Dialectica 67 (4):455-483.score: 18.0
    The common account of the analog vs digital distinction is based on features of physical systems, being related to the usage of continuous vs discrete supports respectively. It is proposed here to alternatively characterize the concepts of analog and digital as related to coding systems, of which a formal definition is given, by suggesting that the distinction refers to the strategy adopted to define the coding function: extensional in digital systems, isomorphic intensional in analog systems. This thesis (...)
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  5. Whit Schonbein (forthcoming). Varieties of Analog and Digital Representation. Minds and Machines:1-24.score: 18.0
    The ‘received view’ of the analog–digital distinction holds that analog representations are continuous while digital representations are discrete. In this paper I first provide support for the received view by showing how it (1) emerges from the theory of computation, and (2) explains engineering practices. Second, I critically assess several recently offered alternatives, arguing that to the degree they are justified they demonstrate not that the received view is incorrect, but rather that distinct senses of the terms have (...)
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  6. Allison M. Barnard, Kelly D. Hughes, Regina R. Gerhardt, Louis DiVincenti Jr, Jenna M. Bovee & Jessica Francine Cantlon (2013). Inherently Analog Quantity Representations in Olive Baboons (Papio Anubis). Frontiers in Psychology 4.score: 18.0
    Strong evidence indicates that non-human primates possess a numerical representation system, but the inherent nature of that system is still debated. Two cognitive mechanisms have been proposed to account for non-human primate numerical performance: (1) a discrete object-file system limited to quantities 4), or span (small vs. large) numbers of food items presented simultaneously or sequentially. The prediction from the object-file hypothesis is that baboons will only accurately choose the larger quantity in small pairs, but not large or span pairs. (...)
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  7. John Haugeland (1981). Analog and Analog. Philosophical Topics 12 (1):213-226.score: 15.0
  8. Gail A. Bruder & Wayne Silverman (1974). Effects of Familiarity and Sequence Length of Analog Matches in the Simultaneous Matching Task. Journal of Experimental Psychology 102 (5):875.score: 15.0
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  9. K. Meer & M. Gori (2002). A Step Towards a Complexity Theory for Analog Systems. Mathematical Logic Quarterly 48 (S1):45-58.score: 15.0
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  10. Daniel Silva Graça (2004). Some Recent Developments on Shannon's General Purpose Analog Computer. Mathematical Logic Quarterly 50 (4‐5):473-485.score: 15.0
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  11. Jacob Beck (forthcoming). Analogue Magnitude Representations: A Philosophical Introduction. British Journal for the Philosophy of Science.score: 14.0
    Empirical discussions of mental representation appeal to a wide variety of representational kinds. Some of these kinds, such as the sentential representations underlying language use and the pictorial representations of visual imagery, are thoroughly familiar to philosophers. Others have received almost no philosophical attention at all. Included in this latter category are analogue magnitude representations, which enable a wide range of organisms to primitively represent spatial, temporal, numerical, and related magnitudes. This paper aims to introduce analogue magnitude representations to a (...)
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  12. David J. Chalmers, Analog Vs. Digital Computation.score: 12.0
    It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue of discreteness vs (...)
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  13. Bruce J. MacLennan (1993). Grounding Analog Computers. Think 2:8-51.score: 12.0
    In this commentary on Harnad's "Grounding Symbols in the Analog World with Neural Nets: A Hybrid Model," the issues of symbol grounding and analog (continuous) computation are separated, it is argued that symbol graounding is as important an issue for analog cognitive models as for digital (discrete) models. The similarities and differences between continuous and discrete computation are discussed, as well as the grounding of continuous representations. A continuous analog of the Chinese Room is presented.
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  14. Francesco Berto & Jacopo Tagliabue (2014). The World is Either Digital or Analogue. Synthese 191 (3):481-497.score: 12.0
    We address an argument by Floridi (Synthese 168(1):151–178, 2009; 2011a), to the effect that digital and analogue are not features of reality, only of modes of presentation of reality. One can therefore have an informational ontology, like Floridi’s Informational Structural Realism, without commitment to a supposedly digital or analogue world. After introducing the topic in Sect. 1, in Sect. 2 we explain what the proposition expressed by the title of our paper means. In Sect. 3, we describe Floridi’s argument. In (...)
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  15. John R. Pani (2002). Mental Imagery is Simultaneously Symbolic and Analog. Behavioral and Brain Sciences 25 (2):205-206.score: 12.0
    With admirable clarity, Pylyshyn shows that there is little evidence that mental imagery is strongly constrained to be analog. He urges that imagery must be considered part of a more general symbolic system. The ultimate solution to the challenges of image theory, however, rest on understanding the manner in which mental imagery is both a symbolic and an analog system.
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  16. Kazuyuki Aihara & Jun Kyung Ryeu (2001). Chaotic Neurons and Analog Computation. Behavioral and Brain Sciences 24 (5):810-811.score: 12.0
    Chaotic dynamics can be related to analog computation. A possibility of electronically implementing the chaos-driven contracting system in the target article is explored with an analog electronic circuit with inevitable noise from the viewpoint of analog computation with chaotic neurons.
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  17. Wim Fias & Tom Verguts (2008). Not All Basic Number Representations Are Analog: Place Coding as a Precursor of the Natural Number System. Behavioral and Brain Sciences 31 (6):650-651.score: 12.0
    Rips et al.'s arguments for rejecting basic number representations as a precursor of the natural number system are exclusively based on analog number coding. We argue that these arguments do not apply to place coding, a type of basic number representation that is not considered by Rips et al.
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  18. Franz Embacher (1984). The Analog of Electric and Magnetic Fields in Stationary Gravitational Systems. Foundations of Physics 14 (8):721-738.score: 12.0
    Newtonian and Machian aspects of the stationary gravitational field are brought into formal analogy with a stationary electromagnetic field. The electromagnetic vector potential equals (up to a factor) the timelike Killing vector field. The current density is given by the contraction of the Killing vector with the Ricci tensor. A coordinate-dependent split in electric and magnetic field vectors is given, and some results of classical electrodynamics are used to illustrate the analogy. In the linearized theory, the usual Maxwell equations are (...)
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  19. Bruce J. MacLennan, Grounding Analog Computers Commentary on Harnad on Symbolism- Connectionism.score: 12.0
    The issue of symbol grounding is not essentially different in analog and digital computation. The principal difference between the two is that in analog computers continuous variables change continuously, whereas in digital computers discrete variables change in discrete steps (at the relevant level of analysis). Interpretations are imposed on analog computations just as on digital computations: by attaching meanings to the variables and the processes defined over them. As Harnad (2001) claims, states acquire intrinsic meaning through their (...)
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  20. James Mattingly & Walter Warwick (2009). Projectible Predicates in Analogue and Simulated Systems. Synthese 169 (3):465 - 482.score: 12.0
    We investigate the relationship between two approaches to modeling physical systems. On the first approach, simplifying assumptions are made about the level of detail we choose to represent in a computational simulation with an eye toward tractability. On the second approach simpler, analogue physical systems are considered that have more or less well-defined connections to systems of interest that are themselves too difficult to probe experimentally. Our interest here is in the connections between the artifacts of modeling that appear in (...)
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  21. John-Michael Kuczynski (2006). THE ANALOGUE-DIGITAL DISTINCTION AND THE COGENCY OF KANT'S TRANSCENDENTAL ARGUMENTS. Existentia: An International Journal of Philosophy:279-320.score: 12.0
    Hume's attempt to show that deduction is the only legitimate form of inference presupposes that enumerative induction is the only non-deductive form of inference. In actuality, enumerative induction is not even a form of inference: all supposed cases of enumerative induction are disguised cases of Inference to the Best Explanation (IBE), so far as they aren't simply cases of mentation of a purely associative kind and, consequently, of a kind that is non-inductive and otherwise non-inferential. The justification for IBE lies (...)
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  22. Argyris Arnellos, Luis Emilio Bruni, Charbel Niño El-Hani & John Collier (2012). Anticipatory Functions, Digital-Analog Forms and Biosemiotics: Integrating the Tools to Model Information and Normativity in Autonomous Biological Agents. Biosemiotics 5 (3):331-367.score: 12.0
    We argue that living systems process information such that functionality emerges in them on a continuous basis. We then provide a framework that can explain and model the normativity of biological functionality. In addition we offer an explanation of the anticipatory nature of functionality within our overall approach. We adopt a Peircean approach to Biosemiotics, and a dynamical approach to Digital-Analog relations and to the interplay between different levels of functionality in autonomous systems, taking an integrative approach. We then (...)
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  23. Mohit Kumar (2002). Low-Voltage Current-Mode Analog Cells. Ratio:1-16.score: 12.0
    This seminar report discusses the low-voltage current-mode analog circuits and their various aspects. The need of high speed, high performance, low power circuits because of the advent of the portable electronic and mobile communication systems and difficulties faced in achieving that in today’s scenario are presented. Current mode circuits are the best suited candidates for the above. Their advantages are discussed here and a comparison with the conventional voltage mode circuits has been presented. The principle and the implementation of (...)
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  24. Chin Hsi Ling Sun Jing (2001). A Voltage Translinear Principle Based Current-Mode Analog Multiplier/Divider. Analysis 3:161-163.score: 12.0
    A novel current-mode analog multiplier/divider based on voltage translinear principle is proposed, featuring favorable precision and wide dynamic range. It is suitable for VLSI implementation and can be successfully applied in a wide range of analog systems such as analog neural network and RMS-to-DC converter.
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  25. Hwee‐Lin Wee, Kok‐Yong Fong, Connie Tse, David Machin, Yin‐Bun Cheung, Nan Luo & Julian Thumboo (2008). Optimizing the Design of Visual Analogue Scales for Assessing Quality of Life: A Semi‐Qualitative Study Among Chinese‐Speaking Singaporeans. Journal of Evaluation in Clinical Practice 14 (1):121-125.score: 10.0
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  26. Chris Eliasmith (2000). Is the Brain Analog or Digital? Cognitive Science Quarterly 1 (2):147-170.score: 9.0
    It will always remain a remarkable phenomenon in the history of philosophy, that there was a time, when even mathematicians, who at the same time were philosophers, began to doubt, not of the accuracy of their geometrical propositions so far as they concerned space, but of their objective validity and the applicability of this concept itself, and of all its corollaries, to nature. They showed much concern whether a line in nature might not consist of physical points, and consequently that (...)
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  27. David Lewis (1971). Analog and Digital. Noûs 5 (3):321-327.score: 9.0
  28. James A. Blachowicz (1997). Analog Representation Beyond Mental Imagery. Journal of Philosophy 94 (2):55-84.score: 9.0
  29. Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.score: 9.0
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog (...)
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  30. Lee A. Rubel (1989). Digital Simulation of Analog Computation and Church's Thesis. Journal of Symbolic Logic 54 (3):1011-1017.score: 9.0
  31. Michael G. Dyer & Boelter Hall, Computationalism, Neural Networks and Minds, Analog or Otherwise.score: 9.0
    A working hypothesis of computationalism is that Mind arises, not from the intrinsic nature of the causal properties of particular forms of matter, but from the organization of matter. If this hypothesis is correct, then a wide range of physical systems (e.g. optical, chemical, various hybrids, etc.) should support Mind, especially computers, since they have the capability to create/manipulate organizations of bits of arbitrarily complexity and dynamics. In any particular computer, these bit patterns are quite physical, but their particular physicality (...)
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  32. Oron Shagrir (2010). Brains as Analog-Model Computers. Studies in History and Philosophy of Science Part A 41 (3):271-279.score: 9.0
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  33. Stevan Harnad (1993). Grounding Symbols in the Analog World with Neural Nets. Think 2 (1):12-78.score: 9.0
    Harnad's main argument can be roughly summarised as follows: due to Searle's Chinese Room argument, symbol systems by themselves are insufficient to exhibit cognition, because the symbols are not grounded in the real world, hence without meaning. However, a symbol system that is connected to the real world through transducers receiving sensory data, with neural nets translating these data into sensory categories, would not be subject to the Chinese Room argument. Harnad's article is not only the starting point for the (...)
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  34. Stevan Harnad, Grounding Symbols in the Analog World with Neural Nets a Hybrid Model.score: 9.0
    1.1 The predominant approach to cognitive modeling is still what has come to be called "computationalism" (Dietrich 1990, Harnad 1990b), the hypothesis that cognition is computation. The more recent rival approach is "connectionism" (Hanson & Burr 1990, McClelland & Rumelhart 1986), the hypothesis that cognition is a dynamic pattern of connections and activations in a "neural net." Are computationalism and connectionism really deeply different from one another, and if so, should they compete for cognitive hegemony, or should they collaborate? These (...)
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  35. James Blachowicz (1997). Analog Representation Beyond Mental Imagery. Journal of Philosophy 94 (2):55 - 84.score: 9.0
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  36. Martin P. Golding (2003). The Legal Analog of the Principle of Bivalence. Ratio Juris 16 (4):450-468.score: 9.0
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  37. V. V. Rybakov (1992). A Modal Analog for Glivenko's Theorem and its Applications. Notre Dame Journal of Formal Logic 33 (2):244-248.score: 9.0
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  38. David Isles (1994). A Finite Analog to the Löwenheim-Skolem Theorem. Studia Logica 53 (4):503 - 532.score: 9.0
    The traditional model theory of first-order logic assumes that the interpretation of a formula can be given without reference to its deductive context. This paper investigates an interpretation which depends on a formula's location within a derivation. The key step is to drop the assumption that all quantified variables must have the same range and to require only that the ranges of variables in a derivation must be related in such way as to preserve the soundness of the inference rules. (...)
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  39. Syed S. Ali & Stuart C. Shapiro (1993). Natural Language Processing Using a Propositional Semantic Network with Structured Variables. Minds and Machines 3 (4):421-451.score: 9.0
    We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structures terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more natural formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena such as pronouns and (...)
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  40. Mauricio Beuchot (2011). The Natural Law as the Philosophical Foundation of Human Rights: Analog Hermeneutics and Ontology. Veritas 25:27-37.score: 9.0
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  41. O. Costa De Beauregard (2004). To Believe or Not Believe in the 4-Potential, That's a Question. The Electric Helmholtz–Mikhailov Effect and its Magnetic Analog. Foundations of Physics 34 (12):1923-1928.score: 9.0
    Helmholtz’ electrically induced extra mass inside a charged hollow sphere, recently evidenced by Mikhailov, is analogous to Mach’s inertial mass. Existence of a corresponding magnetically induced extra mass in an electron flying around an “autistic magnet” is derived. The overall electro-magnetic effect can be covariantly expressed.
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  42. Louise Hay & Douglas Miller (1982). A Topological Analog to the Rice-Shapiro Index Theorem. Journal of Symbolic Logic 47 (4):824-832.score: 9.0
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  43. Steven M. Platek & Gordon G. Gallup (2002). A Self Frozen in Time and Space: Catatonia as a Kinesthetic Analog to Mirrored Self-Misidentification. Behavioral and Brain Sciences 25 (5):589-590.score: 9.0
    Aspects of Northoff's argument lend themselves to the ongoing investigation of localizing the self in the brain. Recent data from the fields of neuropsychology and cognitive neuroscience provide evidence that the right hemisphere is a candidate for localization of self. The data on catatonia further that proposition and add insight into the continuing investigation of self in the brain across sensory and motor domains.
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  44. Erica Hua Fletcher (forthcoming). Dis/Assembling Schizophrenia on YouTube: Theorizing an Analog Body in a Virtual Sphere. Journal of Medical Humanities.score: 9.0
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  45. Kazuyuki Aihara & Jun Kyung Ryeu (2001). Chaotic Neurons and Analog Computation. Behavioral and Brain Sciences 24 (5):810-811.score: 9.0
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  46. June E. Chance, Alvin G. Goldstein & Blake Andersen (1986). Recognition Memory for Infant Faces: An Analog of the Other-Race Effect. Bulletin of the Psychonomic Society 24 (4):257-260.score: 9.0
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  47. Rodrigo Durães de Vasconcellos & Diógenes Cecílio da Silva Júnior (unknown). A 12-Bit Successive Approximation Analog-to-Digital Converter Modeling. Diogenes 55 (31):3409 - 3410.score: 9.0
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  48. Bela Julesz (1983). The Role of Analog Models in Our Digital Age. Behavioral and Brain Sciences 6 (4):668.score: 9.0
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  49. Jerzy Mycka (2006). Analog Computation and Church's Thesis. In A. Olszewski, J. Wole'nski & R. Janusz (eds.), Church's Thesis After Seventy Years. Ontos Verlag. 1--331.score: 9.0
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  50. K. Aihara & J. K. Ryeu (2001). Toward an Interpretation of Dynamic Neural Activity in Terms of Chaotic Dynamical Systems-Open Peer Commentary-Chaotic Neurons and Analog Computation. Behavioral and Brain Sciences 24 (5):810-810.score: 9.0
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