Stable Hebbian learning from spike timing-dependent plasticity

Mark Van Rossum, G. Q. Bi, G. G. Turrigiano

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition Changes in the synaptic connections between neurons are widely believed to contribute to memory storage, and the activitydependent

Original languageEnglish
Pages (from-to)8812-8821
Number of pages10
JournalJournal of Neuroscience
Publication statusPublished - 2000

Keywords / Materials (for Non-textual outputs)

  • Hebbian plasticity
  • synaptic weights
  • synaptic competition
  • activity-dependent scaling
  • temporal learning
  • stochastic approaches


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