Concerning the Neuronal Code

Malsburg, Christoph von der

doi:10.17791/jcs.2018.19.4.511

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Unsupervised learning is one the most prevailing computational models of learning, alongside supervised learning and reinforcement learning models (von der Malsburg, 1973Malsburg, , 1994Malsburg, , 1999Malsburg, , 2018Malsburg, , 2021Willshaw et al, 1997). Whether unsupervised learning is used for human VPL has been empirically tested (Herzog & Fahle, 1998;Shibata et al, 2009Shibata et al, , 2014Tsodyks & Gilbert, 2004;Watanabe & Sasaki, 2015).…”

Section: Discussionmentioning

confidence: 99%

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli

Watanabe,

Sasaki,

Ogawa

et al. 2024

Preprint

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The question of whether we learn exposed visual features remains a subject of controversy. A prevalent computational model suggests that visual features frequently exposed to observers in natural environments are likely to be learned. However, this unsupervised learning model appears to be contradicted by the significant body of experimental results with human participants that indicates visual perceptual learning (VPL) of visible task-irrelevant features does not occur with frequent exposure. Here, we demonstrate a resolution to this controversy with a new finding: Exposure to a dominant global orientation as task-irrelevant leads to VPL of the orientation, particularly when the orientation is derived from natural scene images, whereas VPL did not occur with artificial images even with matched distributions of local orientations and spatial frequencies to natural scene images. Further investigation revealed that this disparity arises from the presence of higher-order statistics derived from natural scene images: global structures such as correlations between different local orientation and spatial frequency channels. Moreover, behavioral and neuroimaging results indicate that the dominant orientation from these higher-order statistics undergoes less attentional suppression than that from artificial images, which may facilitate VPL. Our results contribute to resolving the controversy by affirming the validity of unsupervised learning models for natural scenes but not for artificial stimuli. They challenge the assumption that VPL occurring in everyday life can be predicted by laws governing VPL for conventionally used artificial stimuli.

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Section: Discussionmentioning

confidence: 99%

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli

Watanabe,

Sasaki,

Ogawa

et al. 2024

Preprint

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“…As result of such network self-organization, the brain develops as an overlay of attractor networks ('net fragments') [11]. Each net fragment comprises a set of neurons and the connections among them.…”

Section: Network Self-organization As Kolmogorov Algorithm Of the Brainmentioning

confidence: 99%

A Theory of Natural Intelligence

Malsburg¹,

Stadelmann²,

Grewe³

2022

Preprint

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Introduction:In contrast to current AI technology, natural intelligence -the kind of autonomous intelligence that is realized in the brains of animals and humans to attain in their natural environment goals defined by a repertoire of innate behavioral schemata -is far superior in terms of learning speed, generalization capabilities, autonomy and creativity. How are these strengths, by what means are ideas and imagination produced in natural neural networks? Methods: Reviewing the literature, we put forward the argument that both our natural environment and the brain are of low complexity, that is, require for their generation very little information and are consequently both highly structured. We further argue that the structures of brain and natural environment are closely related. Results: We propose that the structural regularity of the brain takes the form of net fragments (self-organized network patterns) and that these serve as the powerful inductive bias that enables the brain to learn quickly, generalize from few examples and bridge the gap between abstractly defined general goals and concrete situations. Conclusions:Our results have important bearings on open problems in artificial neural network research.

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Vorbild Gehirn – Randbedingungen für eine kognitive Architektur

Malsburg

2020

Cognitive Computing

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Concerning the Neuronal Code

Cited by 4 publications

References 46 publications

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli

A Theory of Natural Intelligence

Vorbild Gehirn – Randbedingungen für eine kognitive Architektur

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