Preprint Open Access

Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks

Spyridon Chavlis; Panayiota Poirazi


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  <identifier identifierType="DOI">10.5281/zenodo.4955397</identifier>
  <creators>
    <creator>
      <creatorName>Spyridon Chavlis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1046-1201</nameIdentifier>
      <affiliation>Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass</affiliation>
    </creator>
    <creator>
      <creatorName>Panayiota Poirazi</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6152-595X</nameIdentifier>
      <affiliation>Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellass'</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Artificial Neural Networks</subject>
    <subject>Biological dendrites</subject>
    <subject>Plasticity</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-06-15</date>
  </dates>
  <resourceType resourceTypeGeneral="Preprint"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4955397</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4955396</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/863245/">863245</awardNumber>
      <awardTitle>A smart, hybrid neural-computo device for drug discovery</awardTitle>
    </fundingReference>
  </fundingReferences>
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