The brain as a self-monitoring system: Recursive instability and AI-augmented control

We propose that failures of biological self-regulation across systems such as Parkinsonian motor rigidity and septic immune collapse can be understood as violations of a shared recursive control condition that governs stable inference: $|1-\alpha g\prime {(\epsilon }_t\left)\right|<1$. This hypothesis reframes perception, introspection, physiological feedback, and AI inference loops as structurally equivalent recursive controllers whose stability depends on how strongly prediction errors update internal models. When this balance is distorted, systems become either over-stable (rigid, unresponsive) or unstable (runaway over-correction). If correct, this framework yields specific implications for translational neuromodulation. Adaptive deep brain stimulation should monitor stability-related quantities—prediction-error gain, update sensitivity, and latent causal structure—rather than relying solely on biomarker thresholds. Causal-latent controllers (e.g., SCDM-like disentanglement models) are predicted to outperform conventional controllers by maintaining stable recursion under perturbation. Conversely, empirical conditions that fail to show improved stability would count against the hypothesis. We outline the theoretical motivation, provide neural and immunological examples, and propose concrete, falsifiable predictions for testing the hypothesis in Parkinson’s disease and sepsis modeling. Finally, we discuss implications for designing AI-augmented closed-loop neuromodulation systems that remain robust under recursive self-monitoring. We hypothesize that failures of biological and artificial self-regulation can be explained by violations of a shared recursive stability condition, in which imbalances between prediction and correction drive systems into rigid or unstable feedback states.