Preprint Open Access
Spyridon Chavlis;
Panayiota Poirazi
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Artificial Neural Networks</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Biological dendrites</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Plasticity</subfield> </datafield> <controlfield tag="005">20210616014822.0</controlfield> <controlfield tag="001">4955397</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass'</subfield> <subfield code="0">(orcid)0000-0001-6152-595X</subfield> <subfield code="a">Panayiota Poirazi</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">519553</subfield> <subfield code="z">md5:0fb4219b1e2330d28e0a4828d50b199e</subfield> <subfield code="u">https://zenodo.org/record/4955397/files/ChavlisPoirazi2021.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2021-06-15</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:4955397</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass</subfield> <subfield code="0">(orcid)0000-0002-1046-1201</subfield> <subfield code="a">Spyridon Chavlis</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks</subfield> </datafield> <datafield tag="536" ind1=" " ind2=" "> <subfield code="c">863245</subfield> <subfield code="a">A smart, hybrid neural-computo device for drug discovery</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.4955396</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.4955397</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">preprint</subfield> </datafield> </record>
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