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Encyclopedia of Computational Neuroscience pp 1–10Cite as

Spike-Timing-Dependent Plasticity, Learning Rules

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Synonyms

Spike-dependent synaptic learning rules; Spike-timing-dependent synaptic plasticity; STDP

Definition

Biological phenomenon. Spike-timing-dependent plasticity (STDP) in its narrow sense refers to the change in the synaptic strength as a result of repeatedly triggering pairs of action potentials (“spikes”) with a fixed time difference between the pre- and postsynaptic action potentials (Markram et al. 1997; Bi and Poo 1998; Sjostrom et al. 2001). STDP is typically observed for synapses between hippocampal or cortical pyramidal neurons in slices of juvenile rodents, and the spike pairings are repeated 50–100 times with various frequencies, e.g. 1 or 10 Hz. This protocol induces a change in the amplitude of a single excitatory postsynaptic potential (EPSP) which is plotted against the spike time difference Δt = t post–t pre between the postsynaptic spike and the presynaptic spike (Fig. 1). The change takes in many cases a few minutes to be expressed and lasts at least for the...

Keywords

  • Spike Train
  • Postsynaptic Activity
  • Postsynaptic Spike
  • Presynaptic Spike
  • Triplet Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Institut für Physiologie, Universität Bern, Bern, Switzerland

    Walter Senn

  2. Theoretical Neuroscience Group, Physiology Department, University of Bern, Bern, Switzerland

    Jean-Pascal Pfister

Authors
  1. Walter Senn
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  2. Jean-Pascal Pfister
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Correspondence to Walter Senn .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Senn, W., Pfister, JP. (2014). Spike-Timing-Dependent Plasticity, Learning Rules. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_683-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_683-1

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  • Online ISBN: 978-1-4614-7320-6

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