Concept transfer of synaptic diversity from biological to artificial neural networks
Abstract Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of b...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60078-9 |
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| _version_ | 1849725059034251264 |
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| author | Martin Hofmann Moritz Franz Peter Becker Christian Tetzlaff Patrick Mäder |
| author_facet | Martin Hofmann Moritz Franz Peter Becker Christian Tetzlaff Patrick Mäder |
| author_sort | Martin Hofmann |
| collection | DOAJ |
| description | Abstract Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of biological neural networks, only a limited number of these concepts have been directly applied to artificial neural networks, with no guarantee of improved performance. Here, we address the discrepancy between the inhomogeneous and dynamic structures of biological neural networks and the largely homogeneous and fixed topologies of artificial neural networks. Specifically, we demonstrate successful integration of concepts of synaptic diversity, including spontaneous spine remodeling, synaptic plasticity diversity, and multi-synaptic connectivity, into artificial neural networks. Our findings reveal increased learning speed, prediction accuracy, and resilience to gradient inversion attacks. Our publicly available drop-in replacement code enables easy incorporation of these proposed concepts into existing networks. |
| format | Article |
| id | doaj-art-65c0f5dc48cf4d3e9db36401a9fdbbe7 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-65c0f5dc48cf4d3e9db36401a9fdbbe72025-08-20T03:10:34ZengNature PortfolioNature Communications2041-17232025-06-0116111610.1038/s41467-025-60078-9Concept transfer of synaptic diversity from biological to artificial neural networksMartin Hofmann0Moritz Franz Peter Becker1Christian Tetzlaff2Patrick Mäder3Data-intensive Systems and Visualization Group (dAI.SY), Technische Universität IlmenauGroup of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center GöttingenGroup of Computational Synaptic Physiology, Department for Neuro- and Sensory Physiology, University Medical Center GöttingenData-intensive Systems and Visualization Group (dAI.SY), Technische Universität IlmenauAbstract Recent developments in artificial neural networks have drawn inspiration from biological neural networks, leveraging the concept of the artificial neuron to model the learning abilities of biological nerve cells. However, while neuroscience has provided new insights into the mechanisms of biological neural networks, only a limited number of these concepts have been directly applied to artificial neural networks, with no guarantee of improved performance. Here, we address the discrepancy between the inhomogeneous and dynamic structures of biological neural networks and the largely homogeneous and fixed topologies of artificial neural networks. Specifically, we demonstrate successful integration of concepts of synaptic diversity, including spontaneous spine remodeling, synaptic plasticity diversity, and multi-synaptic connectivity, into artificial neural networks. Our findings reveal increased learning speed, prediction accuracy, and resilience to gradient inversion attacks. Our publicly available drop-in replacement code enables easy incorporation of these proposed concepts into existing networks.https://doi.org/10.1038/s41467-025-60078-9 |
| spellingShingle | Martin Hofmann Moritz Franz Peter Becker Christian Tetzlaff Patrick Mäder Concept transfer of synaptic diversity from biological to artificial neural networks Nature Communications |
| title | Concept transfer of synaptic diversity from biological to artificial neural networks |
| title_full | Concept transfer of synaptic diversity from biological to artificial neural networks |
| title_fullStr | Concept transfer of synaptic diversity from biological to artificial neural networks |
| title_full_unstemmed | Concept transfer of synaptic diversity from biological to artificial neural networks |
| title_short | Concept transfer of synaptic diversity from biological to artificial neural networks |
| title_sort | concept transfer of synaptic diversity from biological to artificial neural networks |
| url | https://doi.org/10.1038/s41467-025-60078-9 |
| work_keys_str_mv | AT martinhofmann concepttransferofsynapticdiversityfrombiologicaltoartificialneuralnetworks AT moritzfranzpeterbecker concepttransferofsynapticdiversityfrombiologicaltoartificialneuralnetworks AT christiantetzlaff concepttransferofsynapticdiversityfrombiologicaltoartificialneuralnetworks AT patrickmader concepttransferofsynapticdiversityfrombiologicaltoartificialneuralnetworks |