A spike based learning rule for generation of invariant representations

Konrad P. Körding*, Peter König

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

For biological realism, models of learning in neuronal networks often assume that synaptic plasticity solely depends on locally available signals, in particular only on the activity of the pre- and post-synaptic cells. As a consequence, synapses influence the plasticity of other synapses exclusively via the post-synaptic activity. Inspired by recent research on the properties of apical dendrites it has been suggested, that a second integration site in the apical dendrite may mediate specific global information. Here we explore this issue considering the example of learning invariant responses by examining a network of spiking neurones with two sites of synaptic integration. We demonstrate that results obtained in networks of units with continuous outputs transfer to the more realistic neuronal model. This allows a number of more specific experimental predictions, and is a necessary step to unified description of learning rules exploiting timing of action potentials.

Original languageEnglish (US)
Pages (from-to)539-548
Number of pages10
JournalJournal of Physiology Paris
Volume94
Issue number5-6
DOIs
StatePublished - Dec 1 2000

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology (medical)

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