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Phenomenological Model for the Adapatation of Shape-Selective Neurons in Area IT

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

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Abstract

Shape-selective neurons in inferotemporal cortex show adaptation if the same shape stimulus is shown repeatedly. Recent electrophysiological experiments have provided critical data that constrain possible underlying neural mechanisms. We propose a neural model that accounts in a unifying manner for a number of these critical observations. The reproduction of the experimental phenomenology seems to require a combination of input fatigue and firing rate fatigue mechanisms, and the adaptive processes need to be largely independent of the duration of the adapting stimulus. The proposed model realizes these constraints by combining a set of physiologically-inspired mechanisms.

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Acknowledgements

Thanks to L. Fedorov for helpful comments. Funded by EC FP7 ABC PITN-GA-011-290011, HBP FP7-604102; Koroibot FP7-611909, H2020 ICT-644727 CogImon; BMBF FKZ: 01GQ1002A, and DFG GZ: GI 305/4-1 + KA 1258/15-1.

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Authors and Affiliations

  1. Section Compuational Sensomotorics, Department of Cognitive Neurology, CIN&HIH, University Clinic Tübingen, Ottfried-Müller Street 25, 72076, Tübingen, Germany

    Martin A. Giese

  2. Laboratory Neuro en Psychofysiologie, Department of Neuroscience, KU Leuven, Campus Gasthuisberg, O&N 2, Herestraat 49, 3000, Leuven, Belgium

    Pradeep Kuravi & Rufin Vogels

Authors
  1. Martin A. Giese
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  2. Pradeep Kuravi
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  3. Rufin Vogels
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Corresponding author

Correspondence to Martin A. Giese .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Giese, M.A., Kuravi, P., Vogels, R. (2016). Phenomenological Model for the Adapatation of Shape-Selective Neurons in Area IT. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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