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.

Hinton, G. Connectionist learning procedures. Artificial Intelligence, 40:185--234, 1989.
Mitchison, G. Removing time variation with the anti-Hebbian differential synapse. Neural Computation, 3:312--320, 1991.
Földiák, P. Learning invariance from transformation sequences. Neural Computation, 3:194--200, 1991.
Stone, J. and Bray, A. A learning rule for extracting spatio-temporal invariances. Network: Computation in Neural Systems, 6(3):429--436, 1995.
Stone, J. V. Learning perceptually salient visual parameters using spatiotemporal smoothness constraints. Neural Computation, 8:1463--1492, 1996.
Wallis, G. and Rolls, E. Invariant face and object recognition in the visual system. Progress in Neurobiology, 51(2):167--194, 1997.
Peng, H. C., Sha, L. F., Gan, Q. and Wei, Y. Energy function for learning invariance in multilayer perceptron. Electronics Letters, 34(3), 1998.
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.
Stone, J. V. Blind source separation using temporal predictability. Neural Computation, 13:1559--1574, 2001.
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.
Wiskott, L. and Sejnowski, T. Slow feature analysis: unsupervised learning of invariances. Neural Computation, 14(4):715--770, 2002.
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.
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.
Kayser, C., Körding, K. P. and König, P. Learning the nonlinearity of neurons from natural visual stimuli. Neural Computation, 15(8):1751--1759, 2003.
Hurri, J. and Hyvärinen, A. Simple-cell-like receptive fields maximize temporal coherence in natural video. Neural Computation, 15(3):663--691, 2003.
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.
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.
Hashimoto, W. Quadratic forms in natural images. Network: Computation in Neural Systems, 14(4):765--788, 2003.
Wiskott, L. Slow feature analysis: A theoretical analysis of optimal free responses. Neural Computation, 15(9):2147--2177, 9/2003.
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.
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.
Berkes, P. and Wiskott, L. Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision, 5(6):579--602, 2005.
Cox, D., Meier, P., Oertelt, N. and DiCarlo, J. 'Breaking' position-invariant object recognition. Nature Neuroscience, 8:1145--1147, 2005.
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.
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.
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.
Blaschke, T., Berkes, P. and Laurenz, W. What is the relationship between slow feature analysis and independent component analysis?. Neural Computation, 18(10), 2006.
Franzius, M., Sprekeler, H. and Wiskott, L. Slowness leads to place cells. Proc. 15th Annual Computational Neuroscience Meeting, CNS 2006, Edinburgh, Scotland. 2006.
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.
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.
Maurer, A. Unsupervised slow subspace learning from stationary processes. Proceedings of the 17th international conference of algorithmic learning theory, pages 363--377. 2006.
Turner, R. and Sahani, M. A maximum-likelihood interpretation for slow feature analysis. Neural Computation, 19(4):1022--1038, 2007.
Blaschke, T., Zito, T. and Wiskott, L. Independent slow feature analysis and nonlinear blind source separation. Neural Computation, 19(4):994--1021, 2007.
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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