A Neurocognitive Threshold-Adaptation Map for Stress, Resilience, Sensitization, and Shutdown
Abstract
The Neurocognitive Threshold Adaptation Map (NTAM) addresses a fundamental question in systems neuroscience: Can two systems that are indistinguishable in the present behave differently because of the histories that produced them? NTAM operationalizes that question through a deterministic computational framework modeling load, adaptation, retained history, threshold events, and recovery. It is designed to test whether prior states can continue to exert measurable influence on future behavior even after the original conditions have dissipated. The model has been subjected to null controls, hidden-state tests, structured and noisy recovery challenges, dose-response testing, historical-response analysis, large parameter sweeps, and cross-language reproduction. Across those tests, NTAM repeatedly preserved the history-dependent effects it was designed to expose while remaining distinguishable from simpler memoryless alternatives. That behavior is not presented as a single implementation artifact. NTAM has been reproduced across independent Mathematica/Wolfram Language and Python implementations, with NumPy supporting the Python numerical workflow. In the advanced audit series, 280 computational checkpoints converged across both implementations within the defined numerical tolerances, providing a direct test of whether the model’s reported behavior survives translation between computational environments. The resulting package is therefore designed not only to describe NTAM, but to expose it. The equations, code, reference outputs, audit data, parity checks, and reproduction instructions are included so that the model can be independently rerun, altered, challenged, and compared against simpler alternatives. NTAM ultimately makes a testable claim: history can remain dynamically relevant even when the present no longer reveals it directly. This release provides the machinery needed to determine how far that claim survives independent scrutiny.