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Exploring a Mathematical Model of Gain Control via Lateral Inhibition in the Antennal Lobe

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Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

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Abstract

Bioinspired Neural Networks have in many instances paved the way for significant discoveries in Statistical and Machine Learning. Among the many mechanisms employed by biological systems to implement learning, gain control is a ubiquitous and essential component that guarantees standard representation of patterns for improved performance in pattern recognition tasks. Gain control is particularly important for the identification of different odor molecules, regardless of their concentration. In this paper, we explore the functional impact of a biologically plausible model of the gain control on classification performance by representing the olfactory system of insects with a Single Hidden Layer Network (SHLN). Common to all insects, the primary olfactory pathway starts at the Antennal Lobes (ALs) and, then, odor identity is computed at the output of the Mushroom Bodies (MBs). We show that gain-control based on lateral inhibition in the Antennal Lobe robustly solves the classification of highly-concentrated odors. Furthermore, the proposed mechanism does not depend on learning at the AL level, in agreement with biological literature. Due to its simplicity, this bioinspired mechanism may not only be present in other neural systems but can also be further explored for applications, for instance, involving electronic noses.

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Acknowledgments

This research was supported by the Spanish Government projects TIN2010-19607 and TIN2014-54580-R, the predoctoral research grant BES-2011-049274, NIH grant R01GM113967 and CNPq grant 234817/2014-3.

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Authors and Affiliations

  1. Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    Aaron Montero, Ramon Huerta & Francisco B. Rodriguez

  2. BioCircuits Institute, University of California, San Diego, La Jolla, CA, 92093-0402, USA

    Thiago Mosqueiro & Ramon Huerta

Authors
  1. Aaron Montero
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  2. Thiago Mosqueiro
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  3. Ramon Huerta
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  4. Francisco B. Rodriguez
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Corresponding authors

Correspondence to Aaron Montero or Francisco B. Rodriguez .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Montero, A., Mosqueiro, T., Huerta, R., Rodriguez, F.B. (2017). Exploring a Mathematical Model of Gain Control via Lateral Inhibition in the Antennal Lobe. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_28

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_28

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