Next-Generation Neural Mass Models Reproduce Features of Speech Processing

Area of Science:

• Neuroscience
• Computational Neuroscience
• Auditory Processing

Background:

• Syllable segregation is vital for neural speech processing, relying on neural activity aligning with speech rhythms.
• Two main hypotheses, phase-resetting and evoked responses, explain neural speech tracking, but underlying cortical circuits remain unclear.

Purpose of the Study:

• To investigate the cortical mechanisms of neural speech tracking.
• To evaluate if a biophysical neural mass model can replicate key features of neural speech tracking.

Main Methods:

• Compared a biophysical neural mass model against phenomenological models of phase-resetting and evoked responses.
• Assessed model dynamics using in-silico EEG experiments, Phase Concentration Metric, varying syllabic rates, and Inter Event Phase Coherence analysis.

Main Results:

• All models reproduced sharpness-tuned rhythmic speech tracking; the evoked model required pre-processed stimuli.
• The neural mass model demonstrated thresholded phase-resetting triggered by speech envelope onsets.
• This resulted in cross-frequency nested oscillations matching experimental dual-peak Inter Event Phase Coherence signatures.

Conclusions:

• The biophysical neural mass model provides a mechanistic link between cortical population dynamics and speech tracking computations.
• Nonlinear dynamics in the neural mass model explain peak-rate event representations in auditory cortex from continuous acoustic input.