Dendritic normalisation improves learning in sparsely connected artificial neural networks

Alex D. Bird; Peter Jedlicka; Hermann Cuntz

doi:10.1371/journal.pcbi.1009202

Open Access

Peer-reviewed

Research Article

Dendritic normalisation improves learning in sparsely connected artificial neural networks

Alex D. Bird ,

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: bird@fias.uni-frankfurt.de

Affiliations Ernst Strüngmann Institute for Neuroscience (ESI) in co-operation with Max Planck Society, Frankfurt, Germany, Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University Giessen, Giessen, Germany

https://orcid.org/0000-0003-4895-8253

⨯
Peter Jedlicka,

Roles Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – review & editing

Affiliations Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University Giessen, Giessen, Germany

https://orcid.org/0000-0001-6571-5742

⨯
Hermann Cuntz

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing

Affiliations Ernst Strüngmann Institute for Neuroscience (ESI) in co-operation with Max Planck Society, Frankfurt, Germany, Frankfurt Institute for Advanced Studies (FIAS), Frankfurt, Germany

https://orcid.org/0000-0001-5445-0507

⨯

Dendritic normalisation improves learning in sparsely connected artificial neural networks

Alex D. Bird, …
Peter Jedlicka,
Hermann Cuntz

Published: August 9, 2021
https://doi.org/10.1371/journal.pcbi.1009202

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16 Aug 2021
19:17 GMT
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Synaptic weights are known to follow a power-law distribution Posted by gscheler on 16 Aug 2021 at 19:17 GMT

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