Third Institute of Physics and Bernstein Center for Computational Neuroscience, Univ. Göttingen, Göttingen, Germany.
Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania.
PLoS One. 2022 May 26;17(5):e0266679. doi: 10.1371/journal.pone.0266679. eCollection 2022.
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.
依赖于差异Hebbian 规则的尖峰时间依赖可塑性已成为神经元学习的主要范例，因为权重可以根据前后突触信号的时间而增长或收缩。在这里，我们使用这个范例来减少不必要的（声学）噪声。我们的系统依赖于异突触差异Hebbian 学习，我们表明它可以在各种条件下在多麦克风设置下有效地将噪声降低多达-140dB。该系统学习速度很快，通常在几秒钟内即可完成，并且对于不同的几何形状麦克风配置也具有鲁棒性。因此，这项理论研究表明，将源自神经科学的差异Hebbian 学习成功地转移到技术领域是可能的。
