Differential Hebbian learning with time-continuous signals for active noise reduction.
Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0266679 |
| _version_ | 1818048184824365056 |
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| author | Konstantin Möller David Kappel Minija Tamosiunaite Christian Tetzlaff Bernd Porr Florentin Wörgötter |
| author_facet | Konstantin Möller David Kappel Minija Tamosiunaite Christian Tetzlaff Bernd Porr Florentin Wörgötter |
| author_sort | Konstantin Möller |
| collection | DOAJ |
| description | Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain. |
| first_indexed | 2024-12-10T10:17:39Z |
| format | Article |
| id | doaj.art-d0186871c3e4426bace9d06282045fbb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T10:17:39Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d0186871c3e4426bace9d06282045fbb2022-12-22T01:52:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01175e026667910.1371/journal.pone.0266679Differential Hebbian learning with time-continuous signals for active noise reduction.Konstantin MöllerDavid KappelMinija TamosiunaiteChristian TetzlaffBernd PorrFlorentin WörgötterSpike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain.https://doi.org/10.1371/journal.pone.0266679 |
| spellingShingle | Konstantin Möller David Kappel Minija Tamosiunaite Christian Tetzlaff Bernd Porr Florentin Wörgötter Differential Hebbian learning with time-continuous signals for active noise reduction. PLoS ONE |
| title | Differential Hebbian learning with time-continuous signals for active noise reduction. |
| title_full | Differential Hebbian learning with time-continuous signals for active noise reduction. |
| title_fullStr | Differential Hebbian learning with time-continuous signals for active noise reduction. |
| title_full_unstemmed | Differential Hebbian learning with time-continuous signals for active noise reduction. |
| title_short | Differential Hebbian learning with time-continuous signals for active noise reduction. |
| title_sort | differential hebbian learning with time continuous signals for active noise reduction |
| url | https://doi.org/10.1371/journal.pone.0266679 |
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