Differential Hebbian learning with time-continuous signals for active noise reduction

Möller, K., Kappel, D., Tamosiunaite, M., Tetzlaff, C., Porr, B. and Wörgötter, F. (2022) Differential Hebbian learning with time-continuous signals for active noise reduction. PLoS ONE, 17(5), e0266679. (doi: 10.1371/journal.pone.0266679)

Abstract

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.

• Item Type: Articles
• Additional Information: F.W. and C.T. received funding from the European Commission under H2020 grant agreement 899265, FET-Open Project “ADOPD”.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porr, Dr Bernd

Creator Roles

Porr, B.: Methodology, Validation, Writing – original draft

• Authors: Möller, K., Kappel, D., Tamosiunaite, M., Tetzlaff, C., Porr, B., and Wörgötter, F.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: PLoS ONE
• Publisher: Public Library of Science
• ISSN: 1932-6203
• ISSN (Online): 1932-6203
• Copyright Holders: Copyright © 2022 Möller et al.
• First Published: First published in PLoS ONE 17(5): e0266679
• Publisher Policy: Reproduced under a Creative Commons License