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Encyclopedia of Computational Neuroscience pp 1–12Cite as

Hierarchical Models of the Visual System

Synonyms

Deep learning architecture; Hierarchical architectures; Hubel and Wiesel model; Simple-to-complex hierarchy

Definition

Hierarchical models of the visual system are neural networks with a layered topology: In these networks, the receptive fields (i.e., the region of the visual space that units respond to) of units at one level of the hierarchy are constructed by combining inputs from units at a lower level. After a few processing stages, small receptive fields tuned to simple stimuli get combined to form larger receptive fields tuned to more complex stimuli. Such anatomical and functional hierarchical architecture is a hallmark of the organization of the visual system.

Since the pioneering work of Hubel and Wiesel (1962), a variety of hierarchical models have been described from relatively small-scale models of the primary visual cortex to very large-scale (system-level) models of object and action recognition, which account for processing in large portions of the visual field...

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Authors and Affiliations

  1. Institute for Brain Sciences, Brown University, Providence, RI, USA

    Thomas Serre

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  1. Thomas Serre
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Correspondence to Thomas Serre .

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Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Serre, T. (2014). Hierarchical Models of the Visual System. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_345-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-1

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  • Online ISBN: 978-1-4614-7320-6

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Chapter history

  1. Latest

    Hierarchical Models of the Visual System
    Published:
    12 March 2020

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-2

  2. Original

    Hierarchical Models of the Visual System
    Published:
    26 March 2014

    DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-1

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