#37 · Systems biology

Rhythmic dynamics and higher-order information in neural and speech systems

The dominant thread is mechanistic inference from rhythmic and coupled neural or motor dynamics. One preprint reports an alternative representation of discrete articulatory movements that reveals pervasive limit-cycle rhythmicity underlying speech motor activity. A second preprint offers a unifying framework that orders neural mass models, from push-pull oscillators to coupled whole-brain networks, for modelling brain oscillations. A third reviews higher-order information measures, including synergy, redundancy, and partial information decomposition, and their mechanistic interpretation for neural and cognitive data, with attention to its promises and pitfalls. The third item addresses inference methodology rather than oscillation directly, so the shared element is the need to link observed multivariate data to underlying mechanism. What is distinctive is the explicit ordering of oscillator models across scales and the claim of pervasive limit-cycle structure in speech articulation.