#3 · Systems biology

Noise-driven dynamics shape neuronal firing, calcium states, and gait fluctuations

The dominant thread is that fluctuations carry structure that deterministic descriptions can miss. In the gait preprint, ARFIMA decomposition of stride-to-stride series from 70 subjects identifies cued anti-persistence as genuine long memory and reports that DFA overestimates d+0.5. The neuronal preprint simulates a five-dimensional CA1 model with bounded M-current noise and reports coherence resonance and noise-activated firing transitions. The calcium study trains a deep learning classifier on Langevin-simulated data to distinguish eight stochastic intracellular Ca2+ states, reaching 96.8% accuracy. The shared element is a dynamical, stochastic formulation of state. The new element is quantitative discrimination of regimes from fluctuation statistics. The items differ in scale and method. The neuronal and calcium work is simulation-based, and the gait analysis is a model comparison on recorded series.