Spike-timing dependant competitive learning of integrate-and-fire neurons with active dendrites

Christo Panchev, Stefan Wermter, Huixin Chen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

18 Citations (Scopus)


Presented is a model of an integrate-and-fire neuron with active dendrites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested on a neural network with a self-organizing map of competitive neurons. The goal of the presented work is to develop computationally efficient models rather than approximating the real neurons. The approach described in this paper demonstrates the potential advantages of using the processing functionalities of active dendrites as a novel paradigm of computing with networks of artificial spiking neurons.
Original languageEnglish
Title of host publicationArtificial Neural Networks — ICANN 2002
Subtitle of host publicationInternational Conference Madrid, Spain, August 28–30, 2002 Proceedings
EditorsJosé R. Dorronsoro
Place of PublicationBerlin
PublisherSpringer Verlag
Number of pages6
ISBN (Electronic)978-3-540-46084-8
ISBN (Print)978-3-540-44074-1
Publication statusPublished - 2002
Externally publishedYes
EventInternational Conference on Artificial Neural Networks - Madrid, Spain
Duration: 28 Aug 200230 Aug 2002


ConferenceInternational Conference on Artificial Neural Networks
Abbreviated titleICANN 2002


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