Journal ArticleDOI
Learning Invariance Manifolds
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TLDR
A new algorithm for learning invariance manifolds is introduced that allows a neuron to learn a non-linear input–output function to extract invariant or rather slowly varying features from a vectorial input sequence.About:
This article is published in Neurocomputing.The article was published on 1999-06-01. It has received 36 citations till now. The article focuses on the topics: Unsupervised learning & Invariant (physics).read more
Citations
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Journal ArticleDOI
Slow feature analysis yields a rich repertoire of complex cell properties.
Pietro Berkes,Laurenz Wiskott +1 more
TL;DR: This study investigates temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data.
Journal ArticleDOI
Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells
TL;DR: This work presents a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli, which comprises a hierarchy of Slow Feature Analysis nodes.
Journal ArticleDOI
Reinforcement learning on slow features of high-dimensional input streams.
TL;DR: The hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning is supported.
Journal ArticleDOI
Slow feature analysis
TL;DR: Core text and formulas are set in dark red, one can repeat the lecture notes quickly by just reading these, and marks important formulas or items worth remembering and learning for an exam.
References
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Book
The Ecological Approach to Visual Perception
TL;DR: The relationship between Stimulation and Stimulus Information for visual perception is discussed in detail in this article, where the authors also present experimental evidence for direct perception of motion in the world and movement of the self.
Journal ArticleDOI
An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex
J. P. Jones,L. A. Palmer +1 more
TL;DR: It seems that an optimal strategy has evolved for sampling images simultaneously in the 2D spatial and spatial frequency domains and the Gabor function provides a useful and reasonably accurate description of most spatial aspects of simple receptive fields.
Journal ArticleDOI
Neocognitron: A neural network model for a mechanism of visual pattern recognition
TL;DR: In this article, a large-scale network with a learning-with-a-teacher (L2Teacher) process is used for reinforcement of the modifiable synapses in the new large-size model, instead of the learning-without-a teacher process applied to a previous model.
Journal ArticleDOI
Learning invariance from transformation sequences
TL;DR: In this paper, a local learning rule is proposed to learn to generalize across such transformations, where the network is exposed to temporal sequences of patterns undergoing the transformation, and the network learns invariance to shift in retinal position.
Book
Learning invariance from transformation sequences
TL;DR: An application of the algorithm is presented in which the network learns invariance to shift in retinal position, which may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex and also in theDevelopment of more complicated invariance properties of neurons in higher visual areas.