Polyphasic Cybernetics Core — Dynamical Neuroscience Alignment

This paper derives a reduced, canonical polyphasic dynamical system intended to be mathematicallycomparable to standard computational neuroscience models (phase planes, nullclines, excitability,bistability, hysteresis, resonance, and Hopf/saddle-node bifurcations) while remaining compatible with acybernetic implementation (instrumented sensing, regulation, and memory closure). The goal is notmetaphor. The goal is a state-space with explicit variables, observables, and control couplings whosequalitative regimes can be verified using phase portraits and bifurcation diagrams.We show (i) how to define a minimal polyphasic state vector, (ii) how to implement a fast/slowdecomposition consistent with excitability and resonance, (iii) how entropy-driven forcing and regulationcan be expressed as bounded control fields, and (iv) how the same core can be instrumented in software(MGCS/Sentinel-style pipelines) without changing the mathematics.