Kazan (Volga region) Federal University, KFU
KAZAN
FEDERAL UNIVERSITY
 
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MODELING INHIBITORY AND EXCITATORY SYNAPSE LEARNING IN THE MEMRISTIVE NEURON MODEL
Form of presentationArticles in international journals and collections
Year of publication2017
Языканглийский
  • Zykov Evgeniy Yurevich, author
  • Magid Evgeniy Arkadevich, author
  • Talanov Maksim Olegovich, author
  • Bibliographic description in the original language Talanov M., Zykov E., Erokhin V., Magid E., Distefano S., Gerasimov Yu., Vallverdu J. Modeling inhibitory and excitatory synapse learning in the memristive neuron model // ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. - 2017. - Vol.2 - P.514-521.
    Annotation In this paper we present the results of simulation of exitatory Hebbian and inhibitory «sombrero'' learning of a hardware architecture based on organic memristive elements and operational amplifiers implementing an artificial neuron we recently proposed. This is a first step towards the deployment on robots of a bio-plausible simulation, currently developed in the neuro-biologically inspired cognitive architecture (NeuCogAr) implementing basic emotional states or affects in a computational system, in the context of our «Robot dream'' project. The long term goal is to re-implement dopamine, serotonin and noradrenaline pathways of NeuCogAr in a memristive hardware.
    Keywords Cognitive architecture, memristive elements, circuits, artificial neuron, affects, biologically inspired robotic system
    The name of the journal ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029386979&partnerID=40&md5=ffac92e023fb483d67b99a4432c161a9
    Please use this ID to quote from or refer to the card https://repository.kpfu.ru/eng/?p_id=166180&p_lang=2

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