Department of Medical Physiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht, The Netherlands.
Machine Listening Lab, University of Bremen, Germany; Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
Neural Netw. 2022 Aug;152:555-565. doi: 10.1016/j.neunet.2022.05.003. Epub 2022 May 11.
Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The proposed model can extract the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train the network to detect auditory spatial attention. Specifically, our model is composed of three layers, two of which are Integrate and Fire spiking neurons. We formulate a new learning rule that is based on the firing rate of pre- and post-synaptic neurons in the first and second layers of spiking neurons. The third layer has 10 spiking neurons and the pattern of their firing rate is used in the test phase to decode the auditory spatial attention of a given test sample. Moreover, the effects of using low connectivity rates of the layers and specific range of learning parameters of the learning rule are investigated. The proposed model achieves an average accuracy of 90% with only 10% of EEG signals as training data. This study also provides new insights into the role of sparse coding in both cortical networks subserving cognitive tasks and brain-inspired machine learning.
最近的研究表明，alpha 振荡（8-13 Hz）能够解码听觉空间注意。受皮质神经元稀疏编码的启发，我们提出了一种用于听觉空间注意检测的尖峰神经网络模型。所提出的模型可以分别提取记录的左向和右向注意的 EEG 模式，并使用它们来训练网络以检测听觉空间注意。具体来说，我们的模型由三个层组成，其中两个是积分和触发尖峰神经元。我们制定了一个新的学习规则，该规则基于第一层和第二层尖峰神经元中前突触和后突触神经元的放电率。第三层有 10 个尖峰神经元，其放电率模式用于测试阶段，以解码给定测试样本的听觉空间注意。此外，还研究了使用较低的层连接率和学习规则特定学习参数范围的效果。该模型仅使用 10%的 EEG 信号作为训练数据，实现了 90%的平均准确率。这项研究还为稀疏编码在支持认知任务的皮质网络和基于大脑的机器学习中的作用提供了新的见解。
