PRISM-M: A Recurrent Framework for the Formation of Stable Internal Neural Models

Abstract

How transient neural representations become integrated and stable enough to function as internal neural models remains incompletely understood. Grounded in efficient coding, Bayesian and predictive frameworks, recurrent and attractor dynamics, neural state-space models, and systems neuroscience, the Principle of Representation Integration for Stable Models (PRISM) proposes five operations: extraction, compression, integration, stabilization, and prediction/action. Here we developed PRISM-M, a minimal nine-equation recurrent dynamical realization with an explicit contraction condition (0 < J < 1), to examine whether these operations can generate persistent, context-sensitive, and prospectively informative model states. Seven simulation analyses showed persistent but revisable trajectories and a 0.209 context-dependent shift in the event-period model state. A 60 x 60 parameter sweep identified 719 rigid, 2,344 adaptive, and 537 high-gain trajectories within this structurally contractive parameter space, and the same regimes were recovered across 1,000 randomized environments. Perturbations of extraction, integration, and stabilization altered model trajectories, whereas the scalar compression perturbation had a small effect. The full PRISM state predicted the next model state more accurately than the model-state-only baseline (RMSE 0.0186 versus 0.0218), while performing similarly to an unconstrained ARX model. In the BART dataset, spatial fMRI states were distinguishable in 155 participants (69.7% accuracy; 33.3% chance), and inflation-related activity was modestly associated with pumping behavior in the 99 participants with matched behavioral data ({beta} = 0.198, P = 0.040). PRISM-M provides a constrained, testable framework centered on four operational signatures of model-like organization structured representation, contextual integration, persistence or reconstructability, and prospective relevance.