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Cortical reorganization consistent with spike timing–but not correlation-dependent plasticity

Nature Neuroscience volume 10, pages 887–895 (2007)Cite this article

A Corrigendum to this article was published on 01 August 2007

This article has been updated

Abstract

The receptive fields of neurons in primary visual cortex that are inactivated by retinal damage are known to 'shift' to nondamaged retinal locations, seemingly due to the plasticity of intracortical connections. We have observed in cats that these shifts occur in a pattern that is highly convergent, even among receptive fields that are separated by large distances before inactivation. Here we show, using a computational model of primary visual cortex, that the observed convergent shifts are inconsistent with the common assumption that the underlying intracortical connection plasticity is dependent on the temporal correlation of pre- and postsynaptic action potentials. The shifts are, however, consistent with the hypothesis that this plasticity is dependent on the temporal order of pre- and postsynaptic action potentials. This convergent reorganization seems to require increased neuronal gain, revealing a mechanism that networks may use to selectively facilitate the didactic transfer of neuronal response properties.

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Figure 1: Measurement and modeling of retinal lesion–induced receptive field reorganization in V1.
Figure 2: Shifts in receptive field location among neurons in the cortical lesion projection zone of five cats (KR1, KR2, KR4, KR5, KL12).
Figure 3: In vivo and simulated shifts of receptive field projected onto the representation of visual space in V1 (ref.36)
Figure 4: Convergence and divergence among the shifts of in vivo and simulated receptive fields.
Figure 5: Simulated reorganization of receptive fields in a symmetric lesion projection zone, dependent on the strength of neuronal gain and use of CDP or STDP.
Figure 6: Latency of peak responses among the lesion projection zone columns of the model during and after STDP-based reorganization.

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Change history

  • 11 July 2007

    In the version of this article initially published, the author omitted an acknowledgement in the list of acknowledgements at the end of the article. The authors would like to acknowledge financial support contributed by the Berlin Graduate School of Mind and Brain, Germany.

Notes

  1. *NOTE: In the version of this article initially published, the author omitted an acknowledgement in the list of acknowledgements at the end of the article. The authors would like to acknowledge financial support contributed by the Berlin Graduate School of Mind and Brain, Germany.

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Acknowledgements

We thank W. Burke for participating in the experimental work; J.Y. Huang for participating in the control experiments; T. Hoch for modeling advice; and K. Wimmer, E. Mukamel, L. Schwabe, T. Hoch and R. Martin for manuscript comments. Support was contributed by the Australian Research Council, the Bernstein Center for Computational Neuroscience Berlin, the German Federal Ministry of Education and Research (BMBF, grant 10025304), and the German Academic Exchange Service (DAAD).*Footnote 1

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Neural Information Processing Group, Berlin University of Technology, FR 2-1, Franklinstrasse 28/29, Berlin, D-10587, Germany

    Joshua M Young & Klaus Obermayer

  2. Bernstein Center for Computational Neuroscience, Humboldt University Berlin, Philippstrasse 13, House 6, Berlin, 10115, Germany

    Joshua M Young & Klaus Obermayer

  3. Bosch Institute, Anderson Stuart Building (F13), Eastern Avenue, University of Sydney, 2006 New South Wales, Australia

    Joshua M Young, Wioletta J Waleszczyk, Chun Wang & Bogdan Dreher

  4. Nencki Institute of Experimental Biology, Pasteur 3, Warsaw, 02-093, Poland

    Wioletta J Waleszczyk

  5. School of Biomedical Sciences and Hunter Medical Research Institute, Callaghan Campus, The University of Newcastle, Newcastle, New South Wales, 2308, Australia

    Michael B Calford

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Contributions

J.M.Y., W.J.W., C.W. and B.D. conducted the experimental work and analyzed the collected data. M.B.C. made the retinal lesions. B.D. and M.B.C. designed the experiments. K.O. supervised the modeling project, which included providing guidance on the choice of model type and advice on the model's abstractions. J.M.Y. conceived the modeling project and conducted the modeling work, which included developing the model's novel abstractions. The manuscript was drafted primarily by J.M.Y., but all the authors were actively involved in its refinement.

Corresponding authors

Correspondence to Joshua M Young or Klaus Obermayer.

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Supplementary information

Supplementary Fig. 1

Comparison of hand-plotting and automated estimates of receptive field location. (PDF 22 kb)

Supplementary Fig. 2

The correspondence between hand-plotted and automated estimates of receptive field location relative to the distance of estimated receptive field shifts. (PDF 14 kb)

Supplementary Fig. 3

Receptive field position shifts and orientation preference among in vivo neurons within the lesion projection zone. (PDF 22 kb)

Supplementary Fig. 4

Influence of neuronal gain on receptive field reorganization in simulations using spike timing-dependent plasticity. (PDF 128 kb)

Supplementary Methods (PDF 144 kb)

Supplementary Results (PDF 95 kb)

Supplementary Video 1

KR1 (AVI 1040 kb)

Supplementary Video 2

KR2 (AVI 939 kb)

Supplementary Video 3

KR4 (AVI 1425 kb)

Supplementary Video 4

KR5 (AVI 914 kb)

Supplementary Video 5

KL12 (AVI 1486 kb)

Supplementary Videos Legend (PDF 11 kb)

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Young, J., Waleszczyk, W., Wang, C. et al. Cortical reorganization consistent with spike timing–but not correlation-dependent plasticity. Nat Neurosci 10, 887–895 (2007). https://doi.org/10.1038/nn1913

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