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 neurological mechanisms have been suggested: phase-resetting of existing neural oscillations, or evoked responses to acoustic features of the speech signal. From EEG experiment, it is known that a distinct feature of this neural entrainment is that its strength is correlated with phonemes sharpness. Here, we reproduce this EEG experiment in-silico. In particular, we use a biophysical neural mass model to simulate the neural response to the same near-isochronus, consonant-vowel stimuli as the EEG study. We compare this to the response of two other, phenomenological, models that are designed to represent the proposed mechanisms: one a phase-resetting oscillator, the other an evoked-response filter. The biophysical neural mass model captures population-level oscillations without being tailored to speech. All three models can reproduce the correlation between phoneme sharpness and entrainment strength. While both the phase-resetting and evoked models succeed in reproducing the experimental result, the phase-resetting model is arguably more parsimonious since it does not require the preprocessing the evoked model needs to convert a sound envelope into features. Interestingly, the neural mass model also succeeded, without being tailored to the stimulus, unlike the other two models. For example, it does not respond at a specific stimulus-targeted frequency and it is robust across different parameter settings. Our results suggest that generic oscillatory dynamics in cortical populations may be sufficient to generate sharpness-dependent entrainment to speech.