  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.
