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Cited by 501Neural dynamics and brain functionMemory and Neural MechanismsVisual perception and processing mechanisms

The What and Why of Binding

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von der Malsburg, Christoph·1999·Neuron
DOI 10.1016/s0896-6273(00)80825-9
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718 of 718 references verified0 citation errors · 0 not checkable

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  • Knowing only that S is somewhere before H would generate too many false positive coding or binding errors, such as detecting 'sh' in 'sahara'.

    “Knowing that S is somewhere before H (as in an open-bigram coding scheme) would generate too many false positive coding errors or binding errors (von der Malsburg, 1999), such as identifying the presence of the complex grapheme “sh” in the word “sahara.””

    From A Dual-Route Approach to Orthographic Processing (2011) · cites this paper as von der Malsburg, 1999

  • Bayesian belief propagation and particle filtering are the most successful computer vision algorithms to date.

    “Bayesian belief propagation 18 -20 and particle filtering 21,22 are the most successful computer vision algorithms to date.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [19]

  • Algorithms of this type, with separate sets of particles for bottom-up and top-down messages, constitute one of the most promising techniques for statistical inference in large, multilayered domains.

    “Algorithms of this type, although with separate sets of particles for bottom-up and top-down messages, are under active investigation in the computer vision community 19,20,22 and constitute one of the most promising techniques for statistical inference in such large, multilayered domains.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [19]

  • The authors suggest that these ideas could be formulated as a hierarchical Bayesian system and that Bayesian belief propagation and particle filtering are relevant to understanding interactive computations in the visual cortex.

    “Here we suggest that these ideas could be formulated in the form of a hierarchical Bayesian system and that ideas from Bayesian belief propagation 19 and particle filtering 21,23,25 are relevant to understanding these interactive computations in the visual cortex.”

    From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [19]

  • Disparate sensory inputs are processed in different brain regions, at slightly different times, and yet are bound together into unified conscious content ('binding').

    “Disparate sensory inputs are processed in different brain regions, at slightly different times, and yet are bound together into unified conscious content ('binding' [20]).”

    From Consciousness in the universe (2014) · cites this paper as [20]

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Authors
von der Malsburg, C.
Venue
Neuron
Publisher
Elsevier BV
Published
1999-09
Volume
24
Issue
1
Pages
95-104
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/s0896-6273(00)80825-9
OpenAlex
W1967465676
Cited by
501
References
718
Citations by year
2026: 10 · 2025: 14 · 2024: 16 · 2023: 12 · 2022: 12 · 2021: 15 · 2020: 13 · 2019: 12 · 2018: 16 · 2017: 18 · 2016: 15 · 2015: 13 · 2014: 10 · 2013: 14 · 2012: 12
FWCI
2.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
binding, temporal binding, neuronal synchronization, classical neural networks, neural code, brain representation, memory formation, learning mechanisms, computational neuroscience, artificial intelligence, spike trains, face selectivity, superior temporal sulcus, extrastriate cortex
MeSH terms
Animals, Brain, Humans, Mental Processes, Models, Biological, Neurons, Time Factors, Neural Networks, Computer

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