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Authors: Max Talanov 1 ; Evgeniy Zykov 1 ; Victor Erokhin 2 ; Evgeni Magid 3 ; Salvatore Distefano 1 ; Yuriy Gerasimov 1 and Jordi Vallverdú 4

Affiliations: 1 Higher School of Information Technology and Information Systems and Kazan Federal University, Russian Federation ; 2 Institute of Materials for Electronics and Magnetism and Italian National Council of Research, Italy ; 3 Higher Institute for Information Technology and Information Systems and Kazan Federal University, Russian Federation ; 4 Universitat Autonòma de Barcelona, Spain

Keyword(s): Cognitive Architecture, Memristive Elements, Circuits, Artificial Neuron, Affects, Biologically Inspired Robotic System.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Cognitive Robotics ; Informatics in Control, Automation and Robotics ; Modeling, Simulation and Architectures ; Robotics and Automation

Abstract: 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.

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Paper citation in several formats:
Talanov, M.; Zykov, E.; Erokhin, V.; Magid, E.; Distefano, S.; Gerasimov, Y. and Vallverdú, J. (2017). Modeling Inhibitory and Excitatory Synapse Learning in the Memristive Neuron Model. In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-264-6; ISSN 2184-2809, SciTePress, pages 514-521. DOI: 10.5220/0006478805140521

@conference{icinco17,
author={Max Talanov. and Evgeniy Zykov. and Victor Erokhin. and Evgeni Magid. and Salvatore Distefano. and Yuriy Gerasimov. and Jordi Vallverdú.},
title={Modeling Inhibitory and Excitatory Synapse Learning in the Memristive Neuron Model},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2017},
pages={514-521},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006478805140521},
isbn={978-989-758-264-6},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Modeling Inhibitory and Excitatory Synapse Learning in the Memristive Neuron Model
SN - 978-989-758-264-6
IS - 2184-2809
AU - Talanov, M.
AU - Zykov, E.
AU - Erokhin, V.
AU - Magid, E.
AU - Distefano, S.
AU - Gerasimov, Y.
AU - Vallverdú, J.
PY - 2017
SP - 514
EP - 521
DO - 10.5220/0006478805140521
PB - SciTePress

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