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.

开发出基于生理学的语音识别体系结构，通过深度学习框架进行端到端的梯度下降训练，揭示了中央尖峰神经网络中神经振荡的出现，通过测量交互网络层内部和层间的显著交叉频率耦合来处理语音，发现反馈机制的抑制作用对调节和同步神经活动以提高识别性能至关重要，进一步推动了对视听途径同步现象的理解，且架构具有动态和高效的信息处理能力，对神经形态技术具有重要意义。