• Medientyp: E-Book; Hochschulschrift
  • Titel: Margin learning in spiking neural networks
  • Beteiligte: Brune, Rafael [VerfasserIn]; Gütig, Robert [AkademischeR BetreuerIn]; Geisel, Theo [GutachterIn]; Gütig, Robert [GutachterIn]
  • Erschienen: Göttingen, 2017
  • Umfang: 1 Online-Ressource; Illustrationen, Diagramme
  • Sprache: Englisch
  • Identifikator:
  • Schlagwörter: Hochschulschrift
  • Entstehung:
  • Hochschulschrift: Dissertation, Georg-August-Universität Göttingen, 2017
  • Anmerkungen:
  • Beschreibung: The ability to learn, generalize and reliably detect features embedded in continuous sensory input streams is a crucial function of the central nervous system. Sensory neurons process input from thousands of synapses and respond to short features embedded in the input spike stream. Although supervised synaptic learning rules that allow neurons to learn and detect spatio-temporal structures in spike patterns have been developed and studied, it is unclear how neurons can learn to generalize when only a limited set of training examples embedded in high-dimensional input patterns are available....
  • Zugangsstatus: Freier Zugang