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.

翻译：理解大脑的认知过程需要能够大规模复现神经动态的复杂模型。我们提出了一种受生理启发的语音识别架构，该架构与深度学习框架兼容且可扩展，并证明端到端梯度下降训练会导致中枢脉冲神经网络中出现神经振荡。在处理语音过程中，我们测量到网络层内及层间存在显著的交叉频率耦合（这是这些振荡的标志），而在处理背景噪声输入时则未观察到此类相互作用。此外，我们的研究结果强调了反馈机制（如脉冲频率适应和循环连接）在调节和同步神经活动以提升识别性能中的关键抑制作用。总体而言，除了增进我们对人类听觉通路中显著观察到的同步现象的理解外，我们的架构还展现了动态高效的信息处理能力，并与神经形态技术具有相关性。