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A Dynamic Bio-inspired Model of Categorization

  • Conference paper
Neural Information Processing (ICONIP 2012)

Abstract

Motivated by the outstanding performance of primates in pattern recognition tasks, the main purpose of this research is to exploit the behavioral and neuro-biological findings from primates’ visual perception mechanism for categorization applications. Dynamic Bio-Inspired Categorization system (DyBIC) is implemented utilizing nonlinear first order differential equations and its training phase can be accomplished online. The order of the set of differential equations is exclusively a function of the number of categories to be discriminated and the length of the feature vectors doesn’t affect system complexity. Besides, the proposed method carries out recognition in a multi-scale mode which is compatible with some of the well-known cognitive and neural phenomena like categorical perception and hierarchical discrimination. The performance of DyBIC is tested on a handmade typical classification example.

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References

  1. Panchen, A.L.: Classification, Evolution, and the Nature of Biology. Cambridge University Press, Cambridge (1992)

    Book  Google Scholar 

  2. Heisele, B., Serre, T., Pontil, M., Vetter, T., Poggio, T.: Categorization by Learning and Combining Object Parts. In: NIPS, Vancouver (2001)

    Google Scholar 

  3. Schöner, G.: Dynamical Systems Approaches to Cognition. In: Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press, Cambridge (2007)

    Google Scholar 

  4. Gelder, T.V.: The Dynamical Hypothesis in Cognitive Science. Behav. Brain Sci. 21, 615–665 (1998)

    Google Scholar 

  5. Georges, A., Kotliar, G., Krauth, W., Rozenberg, M.J.: Dynamical Mean-Field Theory of Strongly Correlated Fermion Systems and the Limit of Infinite Dimensions. Reviews of Modern Physics 68, 13–125 (1996)

    Article  MathSciNet  Google Scholar 

  6. Fitzgerald, D.J., Sincich, L.C., Sharpee, T.O.: Minimal Models of Multidimensional Computations. PLoS Computational Biology 7 (2011)

    Google Scholar 

  7. Mazzoni, P., Andersen, R.A., Jordon, M.I.: A More Biologically Plausible Learning Rule for Neural Networks. Proc. Natl. Acad. Sci. USA, Neurobiology 88, 4433–4437 (1991)

    Article  Google Scholar 

  8. Moon, T.K.: The Expectation-Maximization Algorithm. IEEE Signal Processing Magazine 13, 47–60 (1996)

    Article  Google Scholar 

  9. Goldstone, R.L., Hendrickson, A.T.: Categorical Perception. Interdisciplinary Reviews: Cognitive Science 1, 65–78 (2010)

    Google Scholar 

  10. Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill, New York (2000)

    Google Scholar 

  11. Dayan, P., Abbott, L.F.: Theoretical Neuroscience. The MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  12. Moldakarimov, S., Roolenhagen, J.E., Olson, C.R., Chow, C.C.: Competitive Dynamics in Cortical Responses to Visual Stimuli. J. Neurophysiol. 94, 3388–3396 (2005)

    Article  Google Scholar 

  13. Gilbert, C., Wiesel, T.N.: Receptive Field Dynamics in Adult Primary Visual Cortex. Nature 356, 150–152 (1992)

    Article  Google Scholar 

  14. Maass, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Network 10, 1659–1671 (1997)

    Article  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Jamalabadi, H., Nasrollahi, H., Ahmadabadi, M.N., Araabi, B.N., Vahabie, A., Abolghasemi, M. (2012). A Dynamic Bio-inspired Model of Categorization. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_20

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  • DOI: https://doi.org/10.1007/978-3-642-34481-7_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34480-0

  • Online ISBN: 978-3-642-34481-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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