Next-Generation Neural Mass Models Reproduce Features of Speech Processing.

Segregation of speech into syllables is a key step in neural speech processing. It relies on the alignment of neural activity with the rhythmic structure of speech. Two competing hypotheses explain this "neural speech tracking", phase-resetting and evoked responses. While phenomenological modeling of these hypotheses has been successful, we still lack understanding of the underlying cortical circuits. To investigate these mechanisms, we evaluate whether a biophysical next-generation neural mass model (NMM) can reproduce several features of neural speech tracking, using phenomenological models of the competing hypotheses as algorithmic baselines. We investigate the models' dynamics with four tests: recreating in silico an EEG experiment that identified a correlation between tracking strength and phoneme sharpness, computing the phase concentration metric, testing the effect of varying syllabic rates, and evaluating the inter event phase coherence (IEPC) across phoneme onsets. While all of the models that we study reproduce the sharpness-tuned rhythmic speech tracking, the evoked model requires a pre-processed acoustic edge impulse stimulus. We demonstrate that the NMM is performing thresholded phase-resetting triggered by sharp onsets in the continuous speech envelope. This produces cross-frequency nested oscillations that qualitatively match an experimentally-observed dual-peak signature in the IEPC. Our results indicate that the biophysical NMM provides a mechanistic bridge between generic oscillatory dynamics in cortical populations and the cognitive computations of speech tracking. Indeed, the nonlinear dynamics of the NMM offer an explanation for how peak-rate event representations in auditory cortex activity arise in response to continuous acoustic input.