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Learning Invariance from Transformation Sequences
Abstract
The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex, and also in the development of more complicated invariance properties of neurons in higher visual areas.
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“This idea of continuity is used in a great number of artificial neural networks and related models (Wiskott and Sejnowski, 2002; Földiák, 2008; Mobahi et al., 2009).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Földiák, 2008
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Multi-task Self-Supervised Visual Learning
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“Foldiak (1991) has proposed computing an average activation of the postsynaptic neuron to assist with the same problem, but the idea here is that the temporal properties of the biologically implemented learning mechanism are such that it is”
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“By presenting consistent sequences of transforming objects the cells in the network can learn to respond to the same object when it is presented in different locations, as described by F€ o oldi a ak (1991), Rolls (1992) and Wallis and Rolls (1997).”
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“Competitive learning mechanisms (e.g. Földiák, 1991) coupled with visual experience may account both for cell selectivity for objects that are frequently seen and for the cell sensitivity to the viewing conditions in which the objects are seen.”
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“The invariance may also be learned in a biophysically plausible way (e.g., Foldiak 1991; Wallis 1996; Wiskott and Sejnowski 2002), during a developmental period, from natural viewing experiences , such as watching a temporal sequence of moving objects .”
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“We believe that continuity of time is the cue that brain uses to solve the invariance problem [5], [18].”
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'Breaking' position-invariant object recognition
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A Canonical Neural Circuit for Cortical Nonlinear Operations
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“We also note that the invariance property can be learned by the similar Hebbian mechanism (Bartlett & Sejnowski, 1998; Földiák, 1991).”
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InfluentialBio-inspired unsupervised learning of visual features leads to robust invariant object recognition
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BackgroundInvariant Visual Responses From Attentional Gain Fields
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Towards cortex sized artificial neural systems
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How are complex cell properties adapted to the statistics of natural stimuli?
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“These studies have successfully applied this criterion to the representations of artificial stimuli such as moving bars to establish that such a mechanism could lead to complex-type neurons (Földiak 1991; Wiskott and Sejnowski 2002).”
BackgroundInfluentialShape Tuning in Macaque Inferior Temporal Cortex
Kayaert, G., Biederman, I., & Vogels, R. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-07-03016.2003 · Cited by 121
“Indeed, single IT neurons may learn to respond similarly to temporally contiguous images (Foldiak, 1991; Wallis, 1998) (for physiological evidence, see Miyashita, 1988).”
BackgroundToward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
“This idea of continuity is used in a great number of artificial neural networks and related models (Wiskott and Sejnowski, 2002; Földiák, 2008; Mobahi et al., 2009).”
MethodPrécis ofNeuroconstructivism: How the Brain Constructs Cognition
Sirois, S., Spratling, M., Thomas, M. S. C., Westermann, G., Mareschal, D., & Johnson, M. H. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0800407x · Cited by 120
“This is achieved through an interactive process involving feedforward and feedback connections between the different cortical areas involved in object perception (Driver et al. 2001).”
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Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition
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Human eye-head co-ordination in natural exploration
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A multiscale dynamic routing circuit for forming size- and position-invariant object representations
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“One promising approach has been described by Foldiak (1991) , who has shown how a complex cell can learn translation invariance by assuming that the presence of an object (or feature) is stable over time.”
BackgroundFace Image Analysis by Unsupervised Learning
Bartlett, M. S. (2001) · Springer eBooks · doi:10.1007/978-1-4615-1637-8 · Cited by 111
“Földíak (Földiák, 1991) demonstrated that Hebbian learning can capture temporal relationships in a feedforward system when the output unit activities undergo temporal smoothing.”
BackgroundMethodInfluentialA Matching Advantage for Dynamic Human Faces
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BackgroundWeakly-Supervised Disentanglement Without CompromisesPreprint
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., & Tschannen, M. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2002.02886 · Cited by 382
“More generally this observation model applies to many natural scenes with moving objects (Földiák, 1991).”
BackgroundA Developmental Approach to Machine Learning?
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“…extended time, the retinal information with respect to that object will necessarily and continuously change, revealing relevant transformations and the invariances for recognition that may be extendable to recognizing novel things (Földiák, 1991; Wiskott and Sejnowski, 2002; Li and DiCarlo, 2008).”
BackgroundCan inhibition resolve retrieval competition through the control of spreading activation?
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“…can still be considered important, in the sense that it keeps categories separate and allows a winner-takes-all strategy to be implemented; that is, when one category is activated, it lessens the likelihood of other categories being similarly activated (see Földiák, 1991; Oram & Földiák, 1996).”
BackgroundA Maximum-Likelihood Interpretation for Slow Feature Analysis
Turner, R. E., & Sahani, M. (2007) · Neural Computation · doi:10.1162/neco.2007.19.4.1022 · Cited by 107
“Observations of this sort motivate temporal constancy, or slowness, as a useful learning principle to extract meaningful higher-level descriptions of sensory data (Hinton, 1989; Földiák, 1991; Mitchison, 1991; Stone, 1996).”
BackgroundSalinas, E., & Abbott, L. F. (2001) · Progress in brain research · doi:10.1016/s0079-6123(01)30012-2 · Cited by 100
Aggregation via Separation: Boosting Facial Landmark Detector With Semi-Supervised Style Translation
Qian, S., Sun, K., Wu, W., Qian, C., & Jia, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01025 · Cited by 99
“Visual invariance of the same instance could be captured in a consecutive sequence of video frames [20, 67, 39, 85, 59, 58, 65].”
BackgroundLearning and neural plasticity in visual object recognition
Kourtzi, Z., & DiCarlo, J. J. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.03.012 · Cited by 95
“At a theoretical level, there is growing appreciation of the potentially powerful role of learning in establishing robust representations crucial for object recognition [7–12].”
BackgroundSimple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video
Hurri, J., & Hyvärinen, A. (2003) · Neural Computation · doi:10.1162/089976603321192121 · Cited by 99
“F(cid:127)oldi(cid:19)ak was one of the (cid:12)rst authors to suggest the usefulness of temporal coherence in computational neuroscience (F(cid:127)oldi(cid:19)ak, 1991).”
BackgroundInvariant Object Recognition in the Visual System with Novel Views of 3D Objects
Stringer, S. M., & Rolls, E. T. (2002) · Neural Computation · doi:10.1162/089976602760407982 · Cited by 97
Unsupervised Learning of Visual Structure using Predictive Generative NetworksPreprint
Lotter, W. E., Kreiman, G., & Cox, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06380 · Cited by 116
Supervised and Unsupervised Learning with Two Sites of Synaptic Integration
Kording, K. P., & Peter König, B. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1013776130161 · Cited by 109
“Variants of these algorithms are based on spatial (Becker and Hinton, 1992; Stone and Bray, 1995), cross-modal (de Sa and Ballard, 1998), or temporal (Földiak, 1991) smoothness criteria and also use a second variable for each cell containing the derivative of a local objective function with respect to the cell’s activ…”
MethodLearning the invariance properties of complex cells from their responses to natural stimuli
Einhäuser, W., Kayser, C., König, P., & Körding, K. P. (2002) · European Journal of Neuroscience · doi:10.1046/j.0953-816x.2001.01885.x · Cited by 91
Perceptual History Influences Neural Responses to Face and Body Postures
Jellema, T., & Perrett, D. I. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/089892903770007353 · Cited by 92
“The notion of the contiguity between different views of moving objects is encapsulated in the ‘‘temporal association hypothesis’’ (Perrett & Oram, 1993; Rolls, 1992), which has been successfully implemented in neural network models of recognition, especially those models that seek to explain transform-invariant object…”
BackgroundRepresentation of Visual Features of Objects in the Inferotemporal Cortex
Tanaka, K. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00045-7 · Cited by 93
Bilinear Sparse Coding for Invariant Vision
Grimes, D. B., & Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/0899766052530893 · Cited by 92
“Examples of this approach include weight sharing and weight decay models Hinton, 1987; Földiák, 1991), the neocognitron (Fukushima, Miyake, & Takayukiito, 1983), LeNet (LeCun et al., 1989), and slow feature analysis (Wiskott & Sejnowski, 2002). A key property of the sparse bilinear model is that it preserves informati…”
MethodNonlinear ICA of temporally dependent stationary sources
Hyvärinen, A., & Morioka, H. (2017) · Työväentutkimus Vuosikirja · Cited by 90
Metadata
- Authors
- Földiák, P.
- Affiliations
- Physiological Laboratory, University of Cambridge, Downing Street, Cambridge CB2 3EG, U.K.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1991-06
- Volume
- 3
- Issue
- 2
- Pages
- 194-200
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2096388912
- Cited by
- 661
- References
- 15
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 10 · 2023: 12 · 2022: 11 · 2021: 20 · 2020: 16 · 2019: 18 · 2018: 21 · 2017: 16 · 2016: 37 · 2015: 20 · 2014: 31 · 2013: 24 · 2012: 28
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- invariance learning, transformation sequences, translation invariance, local learning rules, visual object recognition, complex cells, primary visual cortex, visual system development
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