# A bibliography of slowness

This is a bibliography of papers concerning the *temporal
slowness* principle, sorted by year of publication. This
principle is also known as *temporal stability* or
*temporal coherence* depending on the authors with largely
overlapping meaning.

I'll try to keep this bibliography as complete as possible, so if you happen to know a study which is not included in the list please contact me. When I say "complete" I don't mean "exhaustive": I'm going to include conference contributions or technical reports only if they present data which is not to be found in journal papers.

- [1]
- Hinton, G. Connectionist learning procedures.
*Artificial Intelligence*, 40:185--234, 1989. - [2]
- Mitchison, G. Removing time variation with the anti-Hebbian differential synapse.
*Neural Computation*, 3:312--320, 1991. - [3]
- Földiák, P. Learning invariance from transformation sequences.
*Neural Computation*, 3:194--200, 1991. - [4]
- Stone, J. and Bray, A. A learning rule for extracting spatio-temporal invariances.
*Network: Computation in Neural Systems*, 6(3):429--436, 1995. - [5]
- Stone, J. V. Learning perceptually salient visual parameters using spatiotemporal smoothness constraints.
*Neural Computation*, 8:1463--1492, 1996. - [6]
- Wallis, G. and Rolls, E. Invariant face and object recognition in the visual system.
*Progress in Neurobiology*, 51(2):167--194, 1997. - [7]
- Peng, H. C., Sha, L. F., Gan, Q. and Wei, Y. Energy function for learning invariance in multilayer perceptron.
*Electronics Letters*, 34(3), 1998. - [8]
- Wiskott, L. Learning invariance manifolds. In L. Niklasson, M. Bodén and T. Ziemke, editors,
*Proc. intl. conf. on Artificial Neural Networks, ICANN'98, Skövde*,*Perspectives in Neural Computing*, pages 555--560. 1998. Springer. - [9]
- Stone, J. V. Blind source separation using temporal predictability.
*Neural Computation*, 13:1559--1574, 2001. - [10]
- Kayser, C., Einhäuser, W., Dümmer, O., König, P. and Körding, K. Extracting slow subspaces from natural videos leads to complex cells.
*Artificial Neural Networks - ICANN 2001 Proceedings*, pages 1075--1080. 2001. Springer. - [11]
- Wiskott, L. and Sejnowski, T. Slow feature analysis: unsupervised learning of invariances.
*Neural Computation*, 14(4):715--770, 2002. - [12]
- Berkes, P. and Wiskott, L. Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties.. In J. R. Dorronsoro, editor,
*Artificial neural networks - ICANN 2002 proceedings*,*Lecture Notes in Computer Science*, pages 81--86. 2002. Springer. - [13]
- Bray, A. and Martinez, D. Kernel-based extraction of Slow Features: Complex cells learn disparity and translation invariance from natural images.
*NIPS 2002 proceedings*. 2002. - [14]
- Kayser, C., Körding, K. P. and König, P. Learning the nonlinearity of neurons from natural visual stimuli.
*Neural Computation*, 15(8):1751--1759, 2003. - [15]
- Hurri, J. and Hyvärinen, A. Simple-cell-like receptive fields maximize temporal coherence in natural video.
*Neural Computation*, 15(3):663--691, 2003. - [16]
- Hurri, J. and Hyvärinen, A. Temporal and spatiotemporal coherence in simple-cell responses: a generative model of natural image sequences.
*Network: Computation in Neural Systems*, 14(3):527--551, 2003. - [17]
- Hyvärinen, A., Hurri, J. and Väyrynen, J. Bubbles: a unifying framework for low-level statistical properties of natural image sequences.
*Journal of the Optical Society of America A*, 20(7):1237--1252, 2003. - [18]
- Hashimoto, W. Quadratic forms in natural images.
*Network: Computation in Neural Systems*, 14(4):765--788, 2003. - [19]
- Wiskott, L. Slow feature analysis: A theoretical analysis of optimal free responses.
*Neural Computation*, 15(9):2147--2177, 9/2003. - [20]
- Wiskott, L. Estimating driving forces of nonstationary time series with slow feature analysis. arXiv.org e-Print archive, http://arxiv.org/abs/cond-mat/0312317/, 12/2003.
- [21]
- Körding, K., Kayser, C., Einhäuser, W. and König, P. How are complex cell properties adapted to the statistics of natural scenes?.
*Journal of Neurophysiology*, 91(1):206--212, 2004. - [22]
- Berkes, P. and Wiskott, L. Slow feature analysis yields a rich repertoire of complex cell properties.
*Journal of Vision*, 5(6):579--602, 2005. - [23]
- Cox, D., Meier, P., Oertelt, N. and DiCarlo, J. 'Breaking' position-invariant object recognition.
*Nature Neuroscience*, 8:1145--1147, 2005. - [24]
- Einhäuser, W., Hipp, J., Eggert, J., Körner, E. and König, P. Learning viewpoint invariant object representations using a temporal coherence principle.
*Biological Cybernetics*, 93:79--90, 2005. - [25]
- Hipp, J., Einhäuser, W., Conradt, J. and König, P. Learning of somatosensory representations for texture discrimination using a temporal coherence principle.
*Network: Computation in Neural Systems*, 16(2--3):223--238, 2005. - [26]
- Wyss, R., König, P. and Verschure, P. A model of the ventral visual system based on temporal stability and local memory.
*PLoS Biology*, 4(5):e120, 2006. - [27]
- Blaschke, T., Berkes, P. and Laurenz, W. What is the relationship between slow feature analysis and independent component analysis?.
*Neural Computation*, 18(10), 2006. - [28]
- Franzius, M., Sprekeler, H. and Wiskott, L. Slowness leads to place cells.
*Proc. 15th Annual Computational Neuroscience Meeting, CNS 2006, Edinburgh, Scotland*. 2006. - [29]
- Sprekeler, H. and Wiskott, L. Analytical derivation of complex cell properties from the slowness principle.
*Proc. 15th Annual Computational Neuroscience Meeting, CNS 2006, Edinburgh, Scotland*. 2006. - [30]
- König, P. and Krüger, N. Symbols as self-emergent entities in an optimization process of feature extraction and predictions.
*Biological Cybernetics*, 94(4):325--334, 2006. - [31]
- Maurer, A. Unsupervised slow subspace learning from stationary processes.
*Proceedings of the 17th international conference of algorithmic learning theory*, pages 363--377. 2006. - [32]
- Turner, R. and Sahani, M. A maximum-likelihood interpretation for slow feature analysis.
*Neural Computation*, 19(4):1022--1038, 2007. - [33]
- Blaschke, T., Zito, T. and Wiskott, L. Independent slow feature analysis and nonlinear blind source separation.
*Neural Computation*, 19(4):994--1021, 2007. - [34]
- Sprekeler, H., Michaelis, C. and Wiskott, L. Slowness: An objective for spike-timing-dependent plasticity?.
*PLoS Computational Biology*, 3(6):112, 2007.

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