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Parallels between Machine and Brain Decoding

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Brain Informatics (BI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7670))

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Abstract

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies.

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References

  1. Wiener, N.: Cybernetics: Or the Control and Communication in the Animal and the Machine. MIT Press, Cambridge (1948)

    Google Scholar 

  2. von Neumann, J.: First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15, 27–75 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  3. Turing, A.: Intelligent machinery. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 5, pp. 3–23. Edinburgh University Press, Edinburgh (1969)

    Google Scholar 

  4. Putnam, H.: Minds and Machines. In: Hook, S. (ed.) Dimensions of Mind, pp. 130–164. Collier Books, New York (1960)

    Google Scholar 

  5. Chomsky, N.: Syntactic Structures. The Hague, Mouton (1957)

    Google Scholar 

  6. Chomsky, N.: A Review of B. F. Skinner’s Verbal Behavior. Language 35, 26–58 (1959)

    Article  Google Scholar 

  7. Pinker, S.: How the Mind Works. Norton and Company (2009)

    Google Scholar 

  8. Zanzotto, F.M., Croce, D.: Reading What Machines “Think”. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds.) BI 2009. LNCS (LNAI), vol. 5819, pp. 159–170. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Zanzotto, F.M., Croce, D.: Comparing EEG/ERP-Like and fMRI-Like Techniques for Reading Machine Thoughts. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 133–144. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Prezioso, S., Croce, D., Zanzotto, F.M.: Reading what machines ”think”: a challenge for nanotechnology. Journal of Computational and Theoretical Nanoscience 8, 1–6 (2011)

    Article  Google Scholar 

  11. Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001)

    Article  Google Scholar 

  12. Murphy, B., Baroni, M., Poesio, M.: EEG responds to conceptual stimuli and corpus semantics. In: Proceedings of EMNLP, pp. 619–627. ACL (2009)

    Google Scholar 

  13. Murphy, B., Poesio, M., Bovolo, F., Bruzzone, L., Dalponte, M., Lakany, H.: EEG decoding of semantic category reveals distributed representations for single concepts. Brain and Language 117, 12–22 (2011)

    Article  Google Scholar 

  14. Chan, A.M., Halgren, E., Marinkovic, K., Cash, S.S.: Decoding word and category-specific spatiotemporal representations from MEG and EEG. NeuroImage 54, 3028–3039 (2011)

    Article  Google Scholar 

  15. Sudre, G., Pomerleau, D., Palatucci, M., Wehbe, L., Fyshe, A., Salmelin, R., Mitchell, T.: Tracking Neural Coding of Perceptual and Semantic Features of Concrete Nouns. NeuroImage 62, 451–463 (2012)

    Article  Google Scholar 

  16. Montague, R.: English as a formal language. In: Thomason, R. (ed.) Formal Philosophy: Selected Papers of Richard Montague, pp. 188–221. Yale University Press, New Haven (1974)

    Google Scholar 

  17. Plate, T.A.: Distributed Representations and Nested Compositional Structure. PhD thesis (1994)

    Google Scholar 

  18. Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Proceedings of ACL 2008: HLT, pp. 236–244. Association for Computational Linguistics, Columbus (2008)

    Google Scholar 

  19. Jones, M.N., Mewhort, D.J.K.: Representing word meaning and order information in a composite holographic lexicon. Psychological Review 114, 1–37 (2007)

    Article  Google Scholar 

  20. Zanzotto, F.M., Korkontzelos, I., Fallucchi, F., Manandhar, S.: Estimating linear models for compositional distributional semantics. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING (2010)

    Google Scholar 

  21. Baroni, M., Zamparelli, R.: Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1183–1193. Association for Computational Linguistics, Cambridge (2010)

    Google Scholar 

  22. Guevara, E.: A regression model of adjective-noun compositionality in distributional semantics. In: Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, pp. 33–37. Association for Computational Linguistics, Uppsala (2010)

    Google Scholar 

  23. McCarthy, D., Carroll, J.: Disambiguating nouns, verbs, and adjectives using automatically acquired selectional preferences. Comput. Linguist. 29, 639–654 (2003)

    Article  MATH  Google Scholar 

  24. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  25. Harris, Z.: Distributional structure. In: Katz, J.J., Fodor, J.A. (eds.) The Philosophy of Linguistics. Oxford University Press, New York (1964)

    Google Scholar 

  26. Firth, J.R.: Papers in Linguistics. Oxford University Press, Oxford (1957)

    Google Scholar 

  27. Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Processes VI, 1–28 (1991)

    Google Scholar 

  28. Lund, K., Burgess, C.: Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers 28, 203–208 (1996)

    Article  Google Scholar 

  29. Pado, S., Lapata, M.: Dependency-based construction of semantic space models. Computational Linguistics 33, 161–199 (2007)

    Article  MATH  Google Scholar 

  30. Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  31. Lin, D., Pantel, P.: DIRT-discovery of inference rules from text. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD 2001), San Francisco, CA (2001)

    Google Scholar 

  32. Pakkenberg, B., Gundersen, H.J.: Neocortical neuron number in humans: effect of sex and age. Journal of Comparative Neurology 384, 312–320 (1997)

    Article  Google Scholar 

  33. Stark, A., Toft, M., Pakkenberg, H., Fabricius, K., Eriksen, N., Pelvig, D., Møller, M., Pakkenberg, B.: The effect of age and gender on the volume and size distribution of neocortical neurons. Neuroscience 150, 121–130 (2007)

    Article  Google Scholar 

  34. Azevedo, F.A.C., Carvalho, L.R.B., Grinberg, L.T., Farfel, J.M., Ferretti, R.E.L., Leite, R.E.P., Jacob Filho, W., Lent, R., Herculano-Houzel, S.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513, 532–541 (2009)

    Article  Google Scholar 

  35. Buxhoeveden, D.P., Casanova, M.F.: The minicolumn hypothesis in neuroscience. Brain: A Journal of Neurology 125, 935–951 (2002)

    Article  Google Scholar 

  36. Mountcastle, V.B.: The columnar organization of the neocortex. Brain: A Journal of Neurology 120, 701–722 (1997)

    Article  Google Scholar 

  37. Raz, N., Gunning-Dixon, F., Head, D., Rodrigue, K.M., Williamson, A., Acker, J.D.: Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol. Aging. 25, 377–396 (2004)

    Article  Google Scholar 

  38. Gur, R.C., Turetsky, B.I., Matsui, M., Yan, M., Bilker, W., Hughett, P., Gur, R.E.: Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance. J. Neurosci. 19, 4065–4072 (1999)

    Google Scholar 

  39. Mahon, B.Z., Anzellotti, S., Schwarzbach, J., Zampini, M., Caramazza, A.: Category-specific organization in the human brain does not require visual experience. Neuron 63, 397–405 (2009)

    Article  Google Scholar 

  40. Hagoort, P.: On Broca, brain, and binding: a new framework. Trends in Cognitive Sciences 9, 416–423 (2005)

    Article  Google Scholar 

  41. Yamasaki, S., Yamasue, H., Abe, O., Suga, M., Yamada, H., Inoue, H., Kuwabara, H., Kawakubo, Y., Yahata, N., Aoki, S., Kano, Y., Kato, N., Kasai, K.: Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders. Biol. Psychiatry 68, 1141–1147 (2010)

    Article  Google Scholar 

  42. Alvarado, P., Doerfler, P., Wickel, J.: Axon2 - a visual object recognition system for non-rigid objects. In: IASTED International Conference-Signal Processing, Pattern Recognition and Applications (SPPRA), pp. 235–240. Rhodes, IASTED (2001)

    Google Scholar 

  43. Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann (1993)

    Google Scholar 

  44. John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers, pp. 338–345 (1995)

    Google Scholar 

  45. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  46. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, Chicago (1999)

    Google Scholar 

  47. Miller, G.A.: WordNet: A lexical database for English. Communications of the ACM 38, 39–41 (1995)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Artificial Intelligence Research, University of Rome Tor Vergata, Rome, Italy

    Lorenzo Dell’Arciprete & Fabio Massimo Zanzotto

  2. Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA

    Brian Murphy

Authors
  1. Lorenzo Dell’Arciprete
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  2. Brian Murphy
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  3. Fabio Massimo Zanzotto
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Editor information

Editors and Affiliations

  1. Department of Enterprise Engineering, University of Rome “Tor Vergata”, Viale del Politecnico, 1, 00133, Roma, Italy

    Fabio Massimo Zanzotto

  2. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, 693-8501, Izumo-city, Shimane, Japan

    Shusaku Tsumoto

  3. Department of Artificial Intelligence, Faculty of Mathematics and Natural Sciences, University of Groningen, Nijenborgh 9, 9747 AG, Groningen, Netherlands

    Niels Taatgen

  4. Department of Computer Science, University of Regina, 3737 Wascana Parkway, S4S 0A2, Regina, SK, Canada

    Yiyu Yao

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Dell’Arciprete, L., Murphy, B., Zanzotto, F.M. (2012). Parallels between Machine and Brain Decoding. In: Zanzotto, F.M., Tsumoto, S., Taatgen, N., Yao, Y. (eds) Brain Informatics. BI 2012. Lecture Notes in Computer Science(), vol 7670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35139-6_16

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  • DOI: https://doi.org/10.1007/978-3-642-35139-6_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35138-9

  • Online ISBN: 978-3-642-35139-6

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