Abstract
Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronisation phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.
Abstract (translated)
理解大脑中的认知过程需要复杂且能够在大尺度上复制神经动态的模型。我们提出了一个生理学上启发的语音识别架构，与深度学习框架兼容并具有可扩展性，并证明了端到端梯度下降训练会导致中央尖峰神经网络中神经振荡的出现。在语音处理过程中，我们测量了跨频联系，这些联系表明了这些振荡，而在处理背景噪声输入时，并没有观察到这样的相互作用。此外，我们的研究结果突出了反馈机制（如尖峰频率适应和循环连接）在调节和同步神经活动以提高识别性能中的关键抑制作用。总的来说，在发展我们人类听觉通路中同步现象的基础上，我们的架构表现出动态和高效的信息处理，与类神经形态技术有关。
URL
https://arxiv.org/abs/2404.14024
