Preprint Open Access
Spyridon Chavlis;
Panayiota Poirazi
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.4955397"> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.4955397</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.4955397"/> <dct:creator> <rdf:Description rdf:about="http://orcid.org/0000-0002-1046-1201"> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0002-1046-1201</dct:identifier> <foaf:name>Spyridon Chavlis</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description rdf:about="http://orcid.org/0000-0001-6152-595X"> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">0000-0001-6152-595X</dct:identifier> <foaf:name>Panayiota Poirazi</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass'</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued> <dcat:keyword>Artificial Neural Networks</dcat:keyword> <dcat:keyword>Biological dendrites</dcat:keyword> <dcat:keyword>Plasticity</dcat:keyword> <frapo:isFundedBy rdf:resource="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/863245/"/> <schema:funder> <foaf:Organization> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/100010661</dct:identifier> <foaf:name>European Commission</foaf:name> </foaf:Organization> </schema:funder> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-06-15</dct:issued> <owl:sameAs rdf:resource="https://zenodo.org/record/4955397"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/4955397</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.4955396"/> <dct:description><p>This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adapt them in artificial neurons in order to exploit the respective benefits in machine learning.</p></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:distribution> <dcat:Distribution> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.4955397"/> <dcat:byteSize>519553</dcat:byteSize> <dcat:downloadURL rdf:resource="https://zenodo.org/record/4955397/files/ChavlisPoirazi2021.pdf"/> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> <foaf:Project rdf:about="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/863245/"> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">863245</dct:identifier> <dct:title>A smart, hybrid neural-computo device for drug discovery</dct:title> <frapo:isAwardedBy> <foaf:Organization> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/100010661</dct:identifier> <foaf:name>European Commission</foaf:name> </foaf:Organization> </frapo:isAwardedBy> </foaf:Project> </rdf:RDF>
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