Brain's theta oscillators achieve flexible phase-locking via multi-timescale inhibition
How do cortical oscillators track both fast and slow speech rhythms? A new model reveals the key mechanism.
A new study from Wang and Pittman-Polletta (arXiv 2605.08014) uncovers the dynamical mechanisms behind the brain's ability to phase-lock to rhythmic speech inputs over a wide range of timescales. The researchers used a biophysically grounded cortical theta oscillator model and tools from dynamical systems theory to show that interactions between inhibitory currents operating on multiple timescales enable this flexibility.
Specifically, they found that a theta-timescale (4–8 Hz) inhibitory current (I_m) and a slower delta-timescale (1–4 Hz) potassium current (I_KSS) cooperate to create a three-timescale structure. This interaction gives rise to a delayed Hopf bifurcation phenomenon, which introduces prolonged post-input recovery delays. Interestingly, the superslow I_KSS current plays little role in unforced oscillations but becomes critical under external rhythmic forcing, expanding the phase-locking range. The intermediate I_m current further extends this range by prolonging recovery along the superslow manifold. These results suggest that coordination among multiple inhibitory currents is a key mechanism for flexible speech rhythm tracking in the brain, with potential implications for understanding auditory processing disorders and designing biologically inspired audio processing systems.
- Identified interaction between theta-timescale (I_m, 4–8 Hz) and delta-timescale (I_KSS, 1–4 Hz) inhibitory currents as key for flexible phase-locking.
- Delayed Hopf bifurcation from three-timescale dynamics expands entrainment frequency range by generating prolonged recovery delays.
- Superslow potassium current only recruited under external forcing, enabling flexible synchronization without affecting unforced oscillations.
Why It Matters
Explains how the brain tracks varied speech rhythms, offering insights into auditory disorders and inspiring AI audio models.