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Instant Pattern Filtering and Discrimination in a Multilayer Network with Gaussian Distribution of the Connections

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Pixelization Paradigm (VIEW 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4370))

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Abstract

This paper was designed to build an artificial multilayer network with the purpose of studying abilities like instant pattern recognition and discrimination where no learning would be required. The relevance refers to: (1) theories about putative biological mechanisms that would support innate perception, (2) technological implementation of faster systems for detection and classification of environmental stimulus without learning. Our model was built using few paradigmatic principles of neural organization. The connections obey a Gaussian function. When the network is submitted to diverse input patterns it produces both discriminative and distributed codes in all layers. Contrasting stimulus leads to an attention-like process by salience detection. Finally, the codes always hold a half of all nodes.

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Author information

Authors and Affiliations

  1. Center for Computational Intelligence, Adaptive Systems and Neurophysiology - NIPAN, Department of Physiology, Biological Sciences Institute, Federal University of Juiz de Fora, 36.036-330, Brazil

    Dimitri M. Abramov & Renan W. F. Vitral

  2. Laboratory of Cognitive Physiology, Institute of Biophysics, UFRJ, Brazil

    Dimitri M. Abramov

  3. ICONE-LSI, Department of Electronic Systems, Electric Engineering and Polytechnic, School, USP, Brazil

    Renan W. F. Vitral

Authors
  1. Dimitri M. Abramov
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  2. Renan W. F. Vitral
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Pierre P Lévy Bénédicte Le Grand François Poulet Michel Soto Laszlo Darago Laurent Toubiana Jean-François Vibert

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Abramov, D.M., Vitral, R.W.F. (2007). Instant Pattern Filtering and Discrimination in a Multilayer Network with Gaussian Distribution of the Connections. In: Lévy, P.P., et al. Pixelization Paradigm. VIEW 2006. Lecture Notes in Computer Science, vol 4370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71027-1_21

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  • DOI: https://doi.org/10.1007/978-3-540-71027-1_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71026-4

  • Online ISBN: 978-3-540-71027-1

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