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Slowness: An Objective for Spike-Timing-Dependent Plasticity?

Sprekeler, Henning and Michaelis, Christian and Wiskott, Laurenz (2006) Slowness: An Objective for Spike-Timing-Dependent Plasticity? [Preprint]

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Abstract

Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, and other invariances in a model of the visual system, to the learning of complex cell receptive fields, and, combined with a sparseness objective, to the self-organized formation of place cells in a model of the hippocampus. In order to arrive at a biologically more plausible implementation of this learning rule, we consider analytically how SFA could be realized in simple linear continuous and spiking model neurons. It turns out that for the continuous model neuron SFA can be implemented by means of a modified version of standard Hebbian learning. In this framework we provide a connection to the trace learning rule for invariance learning. We then show that for Poisson neurons spike-timing-dependent plasticity (STDP) with a specific learning window can learn the same weight distribution as SFA. Surprisingly, we find that the appropriate learning rule reproduces the typical STDP learning window. The shape as well as the timescale are in good agreement with what has been measured experimentally. This offers a completely novel interpretation for the functional role of spike-timing-dependent plasticity in physiological neurons.

Item Type:Preprint
Keywords:spike-timing dependent plasticity STDP slowness Slow Feature Analysis SFA invariance learning computational neuroscience modeling trace rule
Subjects:Neuroscience > Neural Modelling
Neuroscience > Computational Neuroscience
Biology > Theoretical Biology
ID Code:5281
Deposited By: Sprekeler, Henning
Deposited On:12 Dec 2006
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Abbott, L. F. and Nelson, S. B. (2000). Synaptic plasticity: Taming the beast. Nature Neuroscience, 3:1178-1183.

Becker, S. and Hinton, G. E. (1992). A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355(6356):161-163.

Berkes, P. and Wiskott, L. (2005). Slow feature analysis yields a rich repertoire of complex cells. Journal of Vision, 5(6):579-602.

Bi, G. and Poo, M. (1998). Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18(24):10464-10472.

Debanne, D., Gähwiler, B. H., and Thomson, S. M. (1994). Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus.PNAS, 91:1148-1152.

Feldman, D. E. (2000). Timing-based LTP and LTD at vertical input to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27:45-56.

Földiak, P. (1989). Adaptive network for optimal linear feature extraction. In Proceedings of the IEEE/INNS International Joint Conference on Neural Networks, pages 401-405, New York. IEEE Press.

Földiak, P. (1991). Learning invariance from transformation sequences. Neural Computation, 3:194-200.

Franzius, M., Sprekeler, H., and Wiskott, L. (2006). Slowness leads to place cells. In Proceedings CNS 2006.

Gütig, R., Aharonov, S., Rotter, S., and Sompolinsky, H. (2003). Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. Journal of Neuroscience, 23(9):3697-3714.

Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195:215-243.

Kempter, R., Gerstner, W., and van Hemmen, J. L. (1999). Hebbian learning and spiking neurons. Physical Review E, 59:4498-4514.

Kistler, W. M. and van Hemmen, J. L. (2000). Modeling synaptic plasticity in conjunction with the timing of pre- and postsynaptic action potentials. Neural Computation, 12:385.

Koch, C., Rapp, M., and Segev, I. (1996). A brief history of time (constants). Cerebral Cortex, 6:92-101.

Körding, K. P. and König, P. (2001). Neurons with two sites of synaptic integration learn invariant representations. Neural Computation, 13(12):2823-2849.

London, M. and Häusser, M. (2005). Dendritic computation. Annu. Rev. Neurosci., 28:503-532.

Markram, H., Lübke, J., Frotscher, M., and Sakmann, B. (1997). Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275:213-215.

Mitchison, G. (1991). Removing time variation with the anti-Hebbian differential synapse. Neural Computation, 3:312-320.

Muller, R., Bostock, E., Taube, J. S., and Kubie, J. L. (1994). On the directional firing properties of hippocampal place cells. Journal of Neuroscience, 14(12):7235-7251.

Oja, E. (1982). A simplified neuron as a principal component analyzer. Journal of Mathematical Biology, 15:267-273.

O'Reilly, R. C. and Johnson, M. H. (1994). Object recognition and sensitive periods: A computational analysis of visual imprinting. Neural Computation, 6(3):357-389.

Peng, H. C., Sha, L. F., Gan, Q., and Wei, Y. (1998). Energy function for learning invariance in multilayer perceptron. Electronics Letters, 34(3):292-294.

Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., and Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435:1102-1107.

Rubin, J., Lee, D. D., and Sompolinsky, H. (2001). Equilibrium properties of temporally asymmetric Hebbian learning. Phys. Rev. Lett., 86(2):364-367.

Sjöström, P. J., Turrigiano, G. G., and Nelson, S. B. (2001). Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32(6):1149-1164.

Song, S. and Abbott, L. F. (2001). Cortical mapping and development through spike timing-dependent plasticity. Neuron, 32:339-350.

Stone, J. V. and Bray, A. (1995). A learning rule for extracting spatio-temporal invariances. Network: Computation in Neural Systems, 6:429-436.

Turrigiano, G. G. and Nelson, S. B. (2000). Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology, 10:358-364.

van Rossum, M. C. W., Bi, G. Q., and Turrigiano, G. G. (2000). Stable Hebbian learning from spike timing-dependent plasticity. Journal of Neuroscience, 20(23):8812-8821.

Wallis, G. and Rolls, E. T. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51(2):167-194.

Wiskott, L. and Sejnowski, T. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4):715-770.

Zhang, L. I., Tao, H. W., Holt, C. E., Harris, W. A., and Poo, M.-m. (1998). A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395(6697):37-44.

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