Implementing classical conditioning with spiking neurons

Chong Liu, Jonathan Shapiro

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we attempt to implement classical conditioning with spiking neurons instead of connectionist neural networks. The neuron model used is a leaky linear integrate-and-fire model with a learning algorithm combining spike-time dependent Hebbian learning and spiketime dependent anti-Hebbian learning. Experimental results show that the major phenomena of classical conditioning, including Pavlovian conditioning, extinction, partial conditioning, blocking, inhibitory conditioning, overshadow and secondary conditioning, can be implemented by the spiking neuron model proposed here and further indicate that spiking neuron models are well suited to implementing classical conditioning. © Springer-Verlag Berlin Heidelberg 2007.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Pages400-410
    Number of pages10
    Volume4668
    ISBN (Print)9783540746898
    DOIs
    Publication statusPublished - 2007
    Event17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto
    Duration: 1 Jul 2007 → …

    Publication series

    NameLecture Notes in Computer Science

    Conference

    Conference17th International Conference on Artificial Neural Networks, ICANN 2007
    CityPorto
    Period1/07/07 → …

    Fingerprint

    Dive into the research topics of 'Implementing classical conditioning with spiking neurons'. Together they form a unique fingerprint.

    Cite this