KFU. Card publication. Modeling inhibitory and excitatory synapse learning in the memristive neuron model

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

MODELING INHIBITORY AND EXCITATORY SYNAPSE LEARNING IN THE MEMRISTIVE NEURON MODEL

Form of presentation

Articles in international journals and collections

Year of publication

2017

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Authors, employees of KFU

Distefano Salvatore , author

Zykov Evgeniy Yurevich, author

Magid Evgeniy Arkadevich, author

Talanov Maksim Olegovich, author

Authors, students KFU

Gerasimov Yuriy Aleksandrovich, 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.scitepress.org/PublicationsDetail.aspx?ID=aQs6etIJ2NQ=&t=1

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=166180&p_lang=2

Resource files

File name	Size (MB)	Format
F_2017_Modeling_inhibitory_and_excitatory_synapse_learning_in_the_memristive_neuron_model.pdf	0,65	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Distefano Salvatore	ru_RU
dc.contributor.author	Zykov Evgeniy Yurevich	ru_RU
dc.contributor.author	Magid Evgeniy Arkadevich	ru_RU
dc.contributor.author	Talanov Maksim Olegovich	ru_RU
dc.contributor.author	Gerasimov Yuriy Aleksandrovich	ru_RU
dc.date.accessioned	2017-01-01T00:00:00Z	ru_RU
dc.date.available	2017-01-01T00:00:00Z	ru_RU
dc.date.issued	2017	ru_RU
dc.identifier.citation	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.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=166180&p_lang=2	ru_RU
dc.description.abstract	ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics	ru_RU
dc.description.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.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Cognitive architecture	ru_RU
dc.subject	memristive elements	ru_RU
dc.subject	circuits	ru_RU
dc.subject	artificial neuron	ru_RU
dc.subject	affects	ru_RU
dc.subject	biologically inspired robotic system	ru_RU
dc.title	Modeling inhibitory and excitatory synapse learning in the memristive neuron model	ru_RU
dc.type	Articles in international journals and collections	ru_RU