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Memorias de investigación
Communications at congresses:
Probabilistic versus incremental presynaptic learning in biological plausible synapses
Year:2011
Research Areas
  • Engineering
Information
Abstract
In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer?s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version.
International
Si
Congress
OWINAC 2011
960
Place
La Palma, Islas Canarias, España
Reviewers
Si
ISBN/ISSN
978-3-642-21343-4
10.1007/978-3-642-21344-1_9
Start Date
30/05/2011
End Date
03/06/2011
From page
80
To page
89
Foundations on Natural and Artificial Computation Lecture Notes in Computer Science, 2011, Volume 6686/2011
Participants
  • Autor: Francisco J Ropero Peláez (Universidad Federal de ABC, Brasil)
  • Autor: Diego Andina De la Fuente (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
  • Departamento: Señales, Sistemas y Radiocomunicaciones
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)