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Spike-timing-dependent synaptic modification induced by natural spike trains

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  • Robert C. Froemke

    (University of California)

  • Yang Dan

    (University of California)

Abstract

The strength of the connection between two neurons can be modified by activity, in a way that depends on the timing of neuronal firing on either side of the synapse1,2,3,4,5,6,7,8,9,10. This spike-timing-dependent plasticity (STDP) has been studied by systematically varying the intervals between pre- and postsynaptic spikes. Here we studied how STDP operates in the context of more natural spike trains. We found that in visual cortical slices the contribution of each pre-/postsynaptic spike pair to synaptic modification depends not only on the interval between the pair, but also on the timing of preceding spikes. The efficacy of each spike in synaptic modification was suppressed by the preceding spike in the same neuron, occurring within several tens of milliseconds. The direction and magnitude of synaptic modifications induced by spike patterns recorded in vivo in response to natural visual stimuli were well predicted by incorporating the suppressive inter-spike interaction within each neuron. Thus, activity-induced synaptic modification depends not only on the relative spike timing between the neurons, but also on the spiking pattern within each neuron. For natural spike trains, the timing of the first spike in each burst is dominant in synaptic modification.

Suggested Citation

  • Robert C. Froemke & Yang Dan, 2002. "Spike-timing-dependent synaptic modification induced by natural spike trains," Nature, Nature, vol. 416(6879), pages 433-438, March.
    • Handle: RePEc:nat:nature:v:416:y:2002:i:6879:d:10.1038_416433a
    DOI: 10.1038/416433a
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    Cited by:

    1. Robert R Kerr & Anthony N Burkitt & Doreen A Thomas & Matthieu Gilson & David B Grayden, 2013. "Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-19, February.
    2. Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Gabriel Koch Ocker & Ashok Litwin-Kumar & Brent Doiron, 2015. "Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-40, August.
    4. Yu, Haitao & Guo, Xinmeng & Wang, Jiang & Deng, Bin & Wei, Xile, 2015. "Spike coherence and synchronization on Newman–Watts small-world neuronal networks modulated by spike-timing-dependent plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 307-317.
    5. Giulia Faini & Dimitrii Tanese & Clément Molinier & Cécile Telliez & Massilia Hamdani & Francois Blot & Christophe Tourain & Vincent Sars & Filippo Bene & Benoît C. Forget & Emiliano Ronzitti & Valent, 2023. "Ultrafast light targeting for high-throughput precise control of neuronal networks," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Yanyun Ren & Xiaobo Bu & Ming Wang & Yue Gong & Junjie Wang & Yuyang Yang & Guijun Li & Meng Zhang & Ye Zhou & Su-Ting Han, 2022. "Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Pierre Yger & Kenneth D Harris, 2013. "The Convallis Rule for Unsupervised Learning in Cortical Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-16, October.
    8. Bernhard Nessler & Michael Pfeiffer & Lars Buesing & Wolfgang Maass, 2013. "Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-30, April.

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