On the performance of the nonsynaptic backpropagation for training long-term cognitive networks

Gonzalo Nápoles, Isel Grau, Leonardo Concepción, Yamisleydi Salgueiro

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    Long-term Cognitive Networks (LTCNs) are recurrent neural networks for modeling and simulation. Such networks can be trained in a synaptic or nonsynaptic mode according to their goal. Nonsynaptic learning refers to adjusting the transfer function parameters while preserving the weights connecting the neurons. In that regard, the Nonsynaptic Backpropagation (NSBP) algorithm has proven successful in training LTCN-based models. Despite NSBP’s success, a question worthy of investigation is whether the backpropagation process is necessary when training these recurrent neural networks. This paper investigates this issue and presents three nonsynaptic learning methods that modify the original algorithm. In addition, we perform a sensitivity analysis of both the NSBP’s hyperparameters and the LTCNs’ learnable parameters. The main conclusions of our study are i) the backward process attached to the NSBP algorithm is not necessary to train these recurrent neural systems, and ii) there is a nonsynaptic learnable parameter that does not contribute significantly to the LTCNs’ performance.

    Original languageEnglish
    Title of host publication11th International Conference of Pattern Recognition Systems, ICPRS 2021
    PublisherInstitution of Engineering and Technology
    Pages25-30
    Number of pages6
    EditionCP773
    ISBN (Electronic)9781839534300, 9781839535048, 9781839536199
    DOIs
    Publication statusPublished - 2021
    Event11th International Conference of Pattern Recognition Systems, ICPRS 2021 - Virtual, Online
    Duration: 17 Mar 202119 Mar 2021

    Publication series

    NameIET Conference Publications
    NumberCP773
    Volume2021

    Conference

    Conference11th International Conference of Pattern Recognition Systems, ICPRS 2021
    CityVirtual, Online
    Period17/03/2119/03/21

    Keywords

    • Long-term cognitive networks
    • Neural cognitive mapping
    • Nonsynaptic learning

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