Noetic Diffusion Theory · unpublished v2 manuscript · checked 2 October 2026

A measured state has coordinates.
Its future needs a law.

Noetic Diffusion Theory asks what a neural system can become from a measured state, and what changes those possibilities. It treats location, local dynamics, future distributions, and their control as separate scientific questions.

The theory asks about latent dynamics. The measurements describe what a declared chart and estimator can support.

One state, different futures

A point alone does not specify what happens next.

Change the drift direction or the stochastic spread. The drawing shows separate parts of an illustrative local law, not a prediction from recorded data.

Conceptual diagram: one shared measured state branches into two bundles of possible short-term paths. The second bundle has a wider spread.
Conceptual simulation The shared point is an illustrative measured coordinate. The controls change toy drift direction and stochastic spread; the paths are not patient data or NMD estimates.
Static diagram shown. The core explanation remains available without JavaScript or WebGL.

A four-part question

From state to conditional access

The questions stay distinct because each needs different measurements, support, and evidence.

01

Where is the measured state?

A declared chart such as MNPS [m, d, e] places an observation in a coordinate system. A map is not the latent process.

02

What is the local dynamical law?

Drift, diffusion, and local sensitivity describe different features of nearby motion in a specified chart.

03

Which futures are accessible?

Predictive spread, reachability, destination probabilities, and response to a finite displacement answer different questions. Each needs its own estimator and support.

04

What changes that access?

Rhythm, history, context, and physiology are candidate controls. Their roles are hypotheses to test, not definitions of NDT.

Explore the project

Keep each layer legible.

Theory

Four layers

The NDT v2 manuscript moves from measured state to local dynamics, possible futures, and conditional access.

Read the theory summary →

Measurement

Chart, support, provenance

NeuralManifoldDynamics turns source recordings into versioned window-level measurements with explicit support and QC.

See the measurement boundary →

Evidence

Results and limits

Empirical associations, estimator failures, and unresolved mechanisms are presented with their claim limits.

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Open record

Published work

The curated Zenodo catalog lists published records and shows when its snapshot was last fetched.

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