title: A spike based learning rule for generation of invariant representations.
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.
authors.affiliation: Institute of Neuroinformatics, ETH/University Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland. peterk@ini.phys.ethz.ch
authors.forename: K P
authors.initials: KP
authors.lastname: Körding
authors.forename: P
authors.initials: P
authors.lastname: König
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D011930:Reaction Time; D013569:Synapses / Q000502:physiology