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Journal articles on the topic 'Computational models'

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Published: 4 June 2021
Last updated: 11 March 2023

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1

Miłkowski, Marcin. "Computational Mechanisms and Models of Computation." Philosophia Scientae, no. 18-3 (October 1, 2014): 215–28. http://dx.doi.org/10.4000/philosophiascientiae.1019.

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2

Humphreys, Paul. "Computational Models." Philosophy of Science 69, S3 (September 2002): S1—S11. http://dx.doi.org/10.1086/341763.

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3

Fellous, Jean-Marc, and Christiane Linster. "Computational Models of Neuromodulation." Neural Computation 10, no. 4 (May 1, 1998): 771–805. http://dx.doi.org/10.1162/089976698300017476.

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Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.
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4

Siegel, David A. "Analyzing Computational Models." American Journal of Political Science 62, no. 3 (June 6, 2018): 745–59. http://dx.doi.org/10.1111/ajps.12364.

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5

Hilgers, M. G. "Computational finance models." IEEE Potentials 19, no. 5 (2001): 8–10. http://dx.doi.org/10.1109/45.890082.

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6

White, D. R., J. C. Buckland-Wright, R. V. Griffith, L. N. Rothenberg, C. K. Showwalter, G. Williams, I. J. Wilson, and M. Zankl. "8. Computational Models." Reports of the International Commission on Radiation Units and Measurements os-25, no. 1 (June 1992): 35–45. http://dx.doi.org/10.1093/jicru_os25.1.35.

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7

White, D. R., J. C. Buckland-Wright, R. V. Griffith, L. N. Rothenberg, C. K. Showwalter, G. Williams, I. J. Wilson, and M. Zankl. "8. Computational Models." Journal of the International Commission on Radiation Units and Measurements os25, no. 1 (June 15, 1992): 35–45. http://dx.doi.org/10.1093/jicru/os25.1.35.

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8

Wienke, B. R. "Computational decompression models." International Journal of Bio-Medical Computing 21, no. 3-4 (November 1987): 205–21. http://dx.doi.org/10.1016/0020-7101(87)90088-2.

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9

Blakey, Ed. "Computational Complexity in Non-Turing Models of Computation." Electronic Notes in Theoretical Computer Science 270, no. 1 (February 2011): 17–28. http://dx.doi.org/10.1016/j.entcs.2011.01.003.

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10

Shi, Qiangqiang, Yiyang Yang, and Xiaolin Li. "EFFICIENCY OF GPU COMPUTATION ON THREE COMPUTATIONAL MODELS." Far East Journal of Applied Mathematics 94, no. 4 (June 29, 2016): 285–316. http://dx.doi.org/10.17654/am094040285.

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11

DONG, ANDY. "Special Issue: Design computing and cognition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 4 (November 2005): 227–28. http://dx.doi.org/10.1017/s0890060405050158.

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The field of research in design computing and cognition focuses on computational theories and systems that enact design. Design computing and cognition produces a unifying framework to model and explain design beyond the description of “design computing and cognition,” as in “design computing” and “design cognition” as two cognate disciplines. Research in design computing and cognition recognizes not only the essential relationship between human cognitive processes as models of computation but also how models of computation inspire conceptual realizations of human cognition in design. The articles in this Special Issue address the concomitant key areas of research in design computing and cognition: computational models of design, computational representations in design, computational design systems, and design cognition. The computationally inspired perspectives, metaphors, models, and theories that the papers deliver create a base for computing and cognition to (re)shape design practice and its role in design science and inquiry.
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12

FERNÁNDEZ, MARIBEL, and IAN MACKIE. "More developments in computational models: introduction." Mathematical Structures in Computer Science 17, no. 4 (August 2007): 585–86. http://dx.doi.org/10.1017/s0960129507006196.

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This second special issue devoted to ‘developments in computational models’ (the first was Volume 16 Issue 4) came out of an open call for papers following the First International Workshop on Developments in Computational Models (DCM). This took place in Lisbon, Portugal, on the 10th July 2005, and was a satellite event of ICALP 2005 focused on abstract models of computation and their associated programming paradigms.
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13

DANTSIN, EVGENY, and ALEXANDER WOLPERT. "A ROBUST DNA COMPUTATION MODEL THAT CAPTURES PSPACE." International Journal of Foundations of Computer Science 14, no. 05 (October 2003): 933–51. http://dx.doi.org/10.1142/s0129054103002096.

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One of the most serious problems in DNA computing is that basic DNA operations are faulty. Many DNA computation models use operations based on annealing and magnetic-beads separation which sometimes produce undesirable results. Some models use reliable operations only but their computational power is too weak. The purpose of this paper is to find a good trade-off between the robustness of DNA operations and their computational power. We present a robust DNA computation model that can solve computationally hard problems. We prove that (i) this model can solve PSPACE-complete problems, and (ii) any computational problem that can be solved with this model is in PSPACE.
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14

Zadiraka, Valerii, Oleksandr Khimich, and Inna Shvidchenko. "Models of Computer Calculations." Cybernetics and Computer Technologies, no. 2 (September 30, 2022): 38–51. http://dx.doi.org/10.34229/2707-451x.22.2.4.

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Introduction. The complexity of computational algorithms for solving typical problems of computational, applied, and discrete mathematics is analyzed from the perspective of the theory of computation, depending on the computer architecture and the used computing model: single-processor, multiprocessor, and quantum. The following classes of problems are considered: systems of linear algebraic equations, the Cauchy problem for systems of ordinary differential equations, numerical integration, boundary value problems for ordinary differential equations, factorization of numbers, finding the discrete logarithm of a number in multiplicative integer groups, searching for the necessary record in an unordered database, etc. The purposes of the paper are: 1. To investigate how the computational complexity depends on the computer architecture and the computational model. 2. To show that the construction of the computational process under the given conditions of calculations is related to the solution of the following problems: – the existence ε-solution to the problem; – the existence of T-effective computing algorithms; – the possibility of building a real computing process under the given computing conditions. 3. To investigate the effect of rounding numbers on computational complexity (especially when solving problems of transcomputational complexity). 4. To give the complexity estimates and total error of the computational algorithm for a number of typical problems of computational, applied, and discrete mathematics. The results. The complexity estimates of computational algorithms of the listed classes of problems for single-processor, multiprocessor and quantum computing models are given. The main focus is on high-performance computing: using the principles of parallel data processing and quantum mechanics. Conclusions. The connection of complexity estimates of computational algorithms with the architecture of computers and models of calculations is demonstrated. The characteristics of the first quantum computers (2016 – 2022), which have gone beyond laboratory research, are given. Keywords: computer technologies, rounding error, sequential, parallel and quantum computing models, complexity estimate.
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15

Antal, P., G. Fannes, B. De Moor, and Y. Moreau. "Probabilistic Graphical Models for Computational Biomedicine." Methods of Information in Medicine 42, no. 02 (2003): 161–68. http://dx.doi.org/10.1055/s-0038-1634328.

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Summary Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. Objectives: Developing a computational framework that is common to the different disciplines that compose computational biomedicine will be a major enabler of the further development and integration of this research domain. Methods: Probabilistic graphical models such as Hidden Markov Models, belief networks, and missing-data models together with computational methods such as dynamic programming, Expectation-Maximization, data-augmentation Gibbs sampling, and the Metropolis-Hastings algorithm provide the tools for an integrated probabilistic approach to computational biomedicine. Results and Conclusions: We show how graphical models have already found a broad application in different fields composing computational biomedicine. We also indicate several challenges that lie at the interface between medical informatics, statistical genomics, and bioinformatics. We also argue that graphical models offer a unified framework making it possible to integrate in a statistically meaningful way multiple models ranging from the molecular level to cellular and to clinical levels. Because of their versatility and firm statistical underpinning, we assert that probabilistic graphical models can serve as the lingua franca for many computationally intensive approaches to biology and medicine. As such, graphical models should be a foundation of the curriculum of students in these fields. From such a foundation, students could then build towards specific computational methods in medical informatics, medical image analysis, statistical genetics, or bioinformatics while keeping the communication open between these areas.
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16

Gide, Milind S., and Linda J. Karam. "Computational Visual Attention Models." Foundations and Trends® in Signal Processing 10, no. 4 (2017): 347–427. http://dx.doi.org/10.1561/2000000055.

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17

Verhaegh, Wim, and Anja van de Stolpe. "Knowledge-based computational models." Oncotarget 5, no. 14 (July 29, 2014): 5196–97. http://dx.doi.org/10.18632/oncotarget.2276.

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18

Niederer, Steven A., Joost Lumens, and Natalia A. Trayanova. "Computational models in cardiology." Nature Reviews Cardiology 16, no. 2 (October 25, 2018): 100–111. http://dx.doi.org/10.1038/s41569-018-0104-y.

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19

Stefanescu, Roxana A., R. G. Shivakeshavan, and Sachin S. Talathi. "Computational models of epilepsy." Seizure 21, no. 10 (December 2012): 748–59. http://dx.doi.org/10.1016/j.seizure.2012.08.012.

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20

Wendling, F. "Computational models of epilepsy." Neurophysiologie Clinique/Clinical Neurophysiology 42, no. 1-2 (January 2012): 75–76. http://dx.doi.org/10.1016/j.neucli.2011.11.015.

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21

Blokpoel, Mark. "Sculpting Computational-Level Models." Topics in Cognitive Science 10, no. 3 (June 27, 2017): 641–48. http://dx.doi.org/10.1111/tops.12282.

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22

Gentner, Dedre, and Kenneth D. Forbus. "Computational models of analogy." Wiley Interdisciplinary Reviews: Cognitive Science 2, no. 3 (September 20, 2010): 266–76. http://dx.doi.org/10.1002/wcs.105.

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23

Geffner, Hector. "Computational models of planning." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 4 (March 18, 2013): 341–56. http://dx.doi.org/10.1002/wcs.1233.

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24

Cortés, J. C., J. V. Romero, M. D. Roselló, Francisco-J. Santonja, and Rafael-J. Villanueva. "Solving Continuous Models with Dependent Uncertainty: A Computational Approach." Abstract and Applied Analysis 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/983839.

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This paper presents a computational study on a quasi-Galerkin projection-based method to deal with a class of systems of random ordinary differential equations (r.o.d.e.’s) which is assumed to depend on a finite number of random variables (r.v.’s). This class of systems of r.o.d.e.’s appears in different areas, particularly in epidemiology modelling. In contrast with the other available Galerkin-based techniques, such as the generalized Polynomial Chaos, the proposed method expands the solution directly in terms of the random inputs rather than auxiliary r.v.’s. Theoretically, Galerkin projection-based methods take advantage of orthogonality with the aim of simplifying the involved computations when solving r.o.d.e.’s, which means to compute both the solution and its main statistical functions such as the expectation and the standard deviation. This approach requires the previous determination of an orthonormal basis which, in practice, could become computationally burden and, as a consequence, could ruin the method. Motivated by this fact, we present a technique to deal with r.o.d.e.’s that avoids constructing an orthogonal basis and keeps computationally competitive even assuming statistical dependence among the random input parameters. Through a wide range of examples, including a classical epidemiologic model, we show the ability of the method to solve r.o.d.e.’s.
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25

Kim, Jun-Sik. "CM-KR-4 On Enhanced Asymptotic Computational Plate Models." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _CM—KR—4–1—_CM—KR—4–2. http://dx.doi.org/10.1299/jsmemecj.2012._cm-kr-4-1.

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26

Berov, Leonid. "Character Focused Narrative Models for Computational Storytelling." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 13, no. 1 (June 25, 2021): 277–79. http://dx.doi.org/10.1609/aiide.v13i1.12909.

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My thesis aims at conceptualizing and implementing a computational model of narrative generation that is informed by narratological theory as well as cognitive multi-agent simulation models. It approaches this problem by taking a mimetic stance towards fictional characters and investigates how narrative phenomena related to characters can be computationally recreated from a deep character model grounded in multi agent systems. Based on such a conceptualization of narrative it explores how the generation of plot can be controlled, and how the quality of the resulting plot can be evaluated, in dependence of fictional characters. By that it contributes to research on computational creativity by implementing an evaluative storytelling system, and to narratology by proposing a generative narrative theory based on several post-structuralist descriptive theories.
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27

Said, Rajab, Philippe Young, Gavin Tabor, and Jonathan Howell. "AN EFFICIENT APPROACH FOR CONVERTING PATIENT-SPECIFIC SCANS INTO HIGHLY ACCURATE COMPUTATIONAL MODELS(1E1 Computational Biomechanics)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2007.3 (2007): S75. http://dx.doi.org/10.1299/jsmeapbio.2007.3.s75.

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28

Kolch, Walter, and Dirk Fey. "Personalized Computational Models as Biomarkers." Journal of Personalized Medicine 7, no. 3 (September 1, 2017): 9. http://dx.doi.org/10.3390/jpm7030009.

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29

Pai, Dinesh. "Computational Models of Human Movement." La lettre du Collège de France, no. 4 (June 1, 2009): 30. http://dx.doi.org/10.4000/lettre-cdf.762.

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30

Mrozek, Adam, and Tadeusz Burczynski. "COMPUTATIONAL MODELS OF POLYCRYSTALLINE MATERIALS." International Journal for Multiscale Computational Engineering 13, no. 2 (2015): 145–61. http://dx.doi.org/10.1615/intjmultcompeng.2015013090.

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31

Okimura, Tsukasa. "Biophysical Models in Computational Psychiatry." Brain & Neural Networks 29, no. 2 (June 5, 2022): 65–77. http://dx.doi.org/10.3902/jnns.29.65.

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32

Melendez, Amanda, David Caballero-Russi, Mariantonieta Gutierrez Soto, and Luis Felipe Giraldo. "Computational models of community resilience." Natural Hazards 111, no. 2 (November 27, 2021): 1121–52. http://dx.doi.org/10.1007/s11069-021-05118-5.

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33

Aidya Hanum Aizam, Nur, and Louis Caccetta. "Computational models for timetabling problem." Numerical Algebra, Control & Optimization 4, no. 3 (2014): 269–85. http://dx.doi.org/10.3934/naco.2014.4.269.

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34

Kaliske, M., C. Jenkel, S. Saft, and E. Resch. "Computational Models for Wooden Structures." Computational Technology Reviews 2 (September 14, 2010): 145–76. http://dx.doi.org/10.4203/ctr.2.7.

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35

Kahana, Michael J. "Computational Models of Memory Search." Annual Review of Psychology 71, no. 1 (January 4, 2020): 107–38. http://dx.doi.org/10.1146/annurev-psych-010418-103358.

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The capacity to search memory for events learned in a particular context stands as one of the most remarkable feats of the human brain. How is memory search accomplished? First, I review the central ideas investigated by theorists developing models of memory. Then, I review select benchmark findings concerning memory search and analyze two influential computational approaches to modeling memory search: dual-store theory and retrieved context theory. Finally, I discuss the key theoretical ideas that have emerged from these modeling studies and the open questions that need to be answered by future research.
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36

Erdemir, A., and H. M. Sauro. "Editorial Reproducibility of Computational Models." IEEE Transactions on Biomedical Engineering 63, no. 10 (October 2016): 1995–96. http://dx.doi.org/10.1109/tbme.2016.2594702.

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37

Valero-Cuevas, F. J., H. Hoffmann, M. U. Kurse, J. J. Kutch, and E. A. Theodorou. "Computational Models for Neuromuscular Function." IEEE Reviews in Biomedical Engineering 2 (2009): 110–35. http://dx.doi.org/10.1109/rbme.2009.2034981.

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38

Wambersie, A., and N. Petoussi. "Computational Models - Summary of Discussion." Radiation Protection Dosimetry 49, no. 1-3 (September 1, 1993): 357–58. http://dx.doi.org/10.1093/oxfordjournals.rpd.a081974.

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39

Loewe, Laurence, and Jane Hillston. "Computational models in systems biology." Genome Biology 9, no. 12 (2008): 328. http://dx.doi.org/10.1186/gb-2008-9-12-328.

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40

Wambersie, A., and N. Petoussi. "Computational Models - Summary of Discussion." Radiation Protection Dosimetry 49, no. 1-3 (September 1, 1993): 357–58. http://dx.doi.org/10.1093/rpd/49.1-3.357.

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41

Tsafnat, G., and E. W. Coiera. "Computational Reasoning across Multiple Models." Journal of the American Medical Informatics Association 16, no. 6 (August 28, 2009): 768–74. http://dx.doi.org/10.1197/jamia.m3023.

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42

FELDMAN, JEROME A. "Computational models and Rethinking innateness." Journal of Child Language 26, no. 1 (February 1999): 217–60. http://dx.doi.org/10.1017/s0305000998243741.

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On the whole, I quite like both the target book (RI) and the review by Matthew Rispoli, but there are some additional observations that might be of interest to the readers of this journal. The whole concept of ‘truth through disputation’ is alien to my scientific tradition and I agree with Rispoli that RI is not helped by the polemical tone.There is a companion volume and software suite by Kim Plunkett & Jeff Elman (1997), Exercises in rethinking innateness, which we have used in an undergraduate cognitive science course. This is, in my opinion, the best source for understanding the main point of RI, which I summarized for the class as: the book tries to show that PDP learning techniques have advanced to the point where we need not assume that tabula rasa learning of language must be ruled out. The Exercises in RI book and particularly doing the on-line examples give the students direct intuition about this claim. Our students were impressed by the Tlearn system, but understood its limitations and were not convinced either of the main claim of RI or of the radical nativist alternative.
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43

Gheorghe, Marian, Mike Holcombe, and Petros Kefalas. "Computational models of collective foraging." Biosystems 61, no. 2-3 (July 2001): 133–41. http://dx.doi.org/10.1016/s0303-2647(01)00164-2.

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44

FERNÁNDEZ, MARIBEL, and IAN MACKIE. "Developments in computational models: introduction." Mathematical Structures in Computer Science 16, no. 04 (July 24, 2006): 553. http://dx.doi.org/10.1017/s0960129506005305.

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45

Kocabas, Sakir. "Computational models of scientific discovery." Knowledge Engineering Review 6, no. 4 (December 1991): 259–305. http://dx.doi.org/10.1017/s0269888900005919.

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AbstractComputational modelling of scientific discovery is emerging as an important research field in artificial intelligence. Various computational systems modelling different aspects of scientific research and discovery have been developed. This paper looks at some of these models in order to examine how knowledge is organized in such systems, what forms of representation they have, how their methods of learning and representation are integrated, and the effects of representation on learning. The paper also describes the achievements and shortcomings of these systems, and discusses the obstacles in developing more comprehensive models.
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46

Flagg, Bob, and Ralph Kopperman. "Computational Models for Ultrametric Spaces." Electronic Notes in Theoretical Computer Science 6 (1997): 151–59. http://dx.doi.org/10.1016/s1571-0661(05)80164-1.

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47

Wendling, Fabrice, Pascal Benquet, Fabrice Bartolomei, and Viktor Jirsa. "Computational models of epileptiform activity." Journal of Neuroscience Methods 260 (February 2016): 233–51. http://dx.doi.org/10.1016/j.jneumeth.2015.03.027.

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48

Korhonen, Rami K., Petro Julkunen, LePing Li, and Corrinus C. van Donkelaar. "Computational Models of Articular Cartilage." Computational and Mathematical Methods in Medicine 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/254507.

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49

Boden, Margaret A. "Multiple Personality and Computational Models." Royal Institute of Philosophy Supplement 37 (March 1994): 103–14. http://dx.doi.org/10.1017/s1358246100010006.

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Some readers may have seen the re-runs, on BBC-TV recently, of the ‘Face to Face’ interviews done by John Freeman in the 1960s. One of these was with the singer Adam Faith, then a startlingly beautiful young man with the grace to be amazed at being chosen to be sandwiched between Martin Luther King and (if I remember aright) J. K. Galbraith. The re-runs were accompanied, where possible, with a further interview with the same person. What I found almost as startling as his lost beauty was Faith's referring to himself-when-young in the third person. After watching the rerun interview, the now middle-aged man commented to Freeman, on several occasions, that ‘He said such-and-such’, ‘He told you so-and-so’, and the like.
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50

Gisiger, T. "Computational models of association cortex." Current Opinion in Neurobiology 10, no. 2 (April 1, 2000): 250–59. http://dx.doi.org/10.1016/s0959-4388(00)00075-1.

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