Scientific Framework for the Study of High-Integration Brain States and Human–AI Symbiosis

1. Institutional Positioning

The Neuroconsciousness Research & Advanced Cognitive Integration Program is a scientific initiative dedicated to the rigorous investigation of:

High-integration brain states (including advanced meditative absorption states traditionally termed Samadhi)
Neural synchronization and large-scale network coherence
Metabolic and electrophysiological modulation of cognition
Brain–computer interface (BCI) optimization
Safe human–AI cognitive integration frameworks

The program operates within established neuroscience, cognitive science, computational modeling, and neuroengineering disciplines.

It does not assume metaphysical explanations.
It seeks measurable, replicable neurophysiological correlates.

2. Conceptual Reframing: From “Bioenergy” to Measurable Neurophysiology

The original “bioenergy accumulation” hypothesis is reframed into operational scientific variables:

Instead of “bioenergy,” we define:

• Cerebral metabolic rate (CMR)
• ATP production dynamics
• Mitochondrial efficiency
• Glucose–oxygen utilization
• Neural oscillatory coherence
• Phase synchrony across large-scale networks
• Functional connectivity density

These are measurable.

No evidence supports a total “neurochemical → neuroelectric phase shift.”
All synaptic transmission remains electrochemical in nature.

However:

Brain states do shift between different regimes of:

Oscillatory dominance
Synchronization patterns
Network modularity
Information integration levels

This is the scientifically viable core.

3. High-Integration Brain States (HIBS)

We define:

High-Integration Brain States (HIBS)
= transient or sustained neural regimes characterized by increased global coherence, large-scale phase synchrony, and altered network topology.

Observed correlates in advanced meditation research:

Increased gamma-band coherence
Reduced default mode network (DMN) dominance
Increased frontoparietal integration
Altered thalamocortical coupling
Reduced prediction error signaling

These findings are partially supported by EEG and fMRI literature in experienced meditators.

No evidence supports universal consciousness expansion in a physical sense.
However, subjective reports of expanded awareness correlate with measurable neural integration changes.

4. Information Integration & Synaptic Dynamics

The claim that synaptic “funnel direction reverses” is not biologically supported.

However, we can reinterpret the idea as:

Increased bidirectional effective connectivity
Reduced hierarchical compression bias
Enhanced global broadcasting (Global Workspace Theory framework)
Increased Integrated Information (IIT-like metrics)

Hypothesis (Testable):

Advanced meditative absorption states increase: $\Phi_{eff} = \text{Effective information integration across cortical networks}$ Φeff=Effective information integration across cortical networks

Measured via:

Transfer entropy
Phase-locking value (PLV)
Granger causality
Dynamic causal modeling

This is scientifically investigable.

5. Metabolic & Electrophysiological Modulation

Instead of “bioenergy pumping,” we examine:

Autonomic regulation
Breath-induced CO₂ modulation
Vagal tone shifts
HRV changes
Neurovascular coupling adjustments
Cerebral blood flow redistribution

Certain breathing techniques can alter:

Blood pH
CO₂ concentration
Cortical excitability thresholds
Oscillatory power spectra

These mechanisms plausibly explain altered conscious states without invoking unverified energy transformations.

6. Electromagnetic Field Claims (Reframed)

The human brain produces measurable electromagnetic fields (EEG/MEG detectable).

However:

There is no evidence of large-scale external EM coupling enabling universal interaction.
Brain EM fields are weak and decay rapidly.

Research direction:

Investigate local field potentials
Study network-level synchrony
Explore whether coherent oscillations increase computational efficiency

No claims of external EM manipulation are retained.

7. Research Architecture

7.1 Experimental Framework

Phase 1: Baseline Neurophenomenology

EEG high-density mapping
MEG phase synchrony analysis
fMRI connectivity mapping
HRV and autonomic profiling

Phase 2: Controlled Modulation

Breathwork protocols
Meditation protocols
Neurofeedback-assisted stabilization

Phase 3: Integration Modeling

Computational neural network modeling
Information integration quantification
Network topology evolution tracking

8. Artificial Augmentation Pathways (Ethically Constrained)

Instead of “extra artificial bioenergy,” we define:

• Non-invasive brain stimulation (tACS, tDCS, TMS)
• Closed-loop neurofeedback
• Real-time oscillatory entrainment
• Neuroadaptive interface systems

These technologies aim to:

Stabilize coherence patterns
Enhance cognitive control
Improve attention regulation

All must pass:

Clinical safety thresholds
Ethical review boards
Long-term follow-up validation

9. Brain–Computer Interface (BCI) Integration

Scientific objective:

Increase signal clarity and stability for BCI communication.

Research hypothesis:

Higher global neural coherence may:

Improve signal-to-noise ratio
Increase decoding accuracy
Reduce latency
Enhance sustained cognitive engagement

Applications:

Assistive technologies
Augmented cognition systems
AI collaborative systems

This is plausible and testable.

10. Genetic Manipulation Section (Revised)

The proposal to genetically reduce “resistance to bioenergy” is removed as incoherent and ethically hazardous.

Genetic research direction (if any):

Study polymorphisms affecting neuroplasticity
BDNF expression variability
Neurotransmitter regulation genes
Mitochondrial efficiency markers

No enhancement-based germline manipulation is endorsed.

Research remains observational and therapeutic.

11. Transhumanization (Reframed as Cognitive Augmentation)

Rather than “new species,” the defensible framework is:

Cognitive Augmentation Phase of Human Evolution

Potential domains:

AI-assisted cognition
Neural prosthetics
Memory support systems
Real-time knowledge integration

This represents cultural–technological evolution, not biological speciation.

12. Ethical Framework

Key principles:

Voluntary participation
Informed consent
No coercive enhancement
Privacy of neural data
Reversibility of interventions
International bioethics compliance

No irreversible modification pathway without multi-layer oversight.

13. Commercial & Institutional Implications

Potential value domains:

Cognitive training platforms
Neurofeedback systems
BCI optimization software
Performance enhancement (non-clinical)
Mental health stabilization tools
AI-human interface protocols

Enterprise model:

Research consortium → Pilot validation → Clinical approval (if medical) → Controlled commercialization.

14. Strategic Positioning Within Maitreya Architecture

This vertical supports:

Human Cognitive Development
Advanced Intelligence Systems
AI Alignment
Long-term Human–Machine Coevolution

It provides scientific legitimacy to contemplative-state research while remaining grounded in measurable neurophysiology.

15. Core Concept

High-integration brain states correlate with increased large-scale neural coherence, altered network topology, and measurable metabolic modulation. These states may enhance cognitive integration and improve human–AI interface stability when studied rigorously within neuroscience and bioengineering frameworks.

16. Final Institutional Statement

The Neuroconsciousness Research & Advanced Cognitive Integration Program aims to scientifically investigate high-coherence brain states traditionally associated with advanced meditation and to evaluate their potential applications in cognitive enhancement, neurotechnology stabilization, and human–AI symbiosis — within strict ethical and empirical boundaries.

The Neurophysiology of High-Integration Brain States:

A Research Framework for Investigating Advanced Meditative Absorption and Human–AI Cognitive Interface Optimization

Authoring Institution: Maitreya Neuroconsciousness Research Division
Manuscript Type: Conceptual Framework & Research Agenda
Keywords: meditation, neural coherence, information integration, brain–computer interface, gamma oscillations, network topology, neurophenomenology, cognitive augmentation

Abstract

Advanced meditative absorption states—traditionally described in contemplative traditions as Samadhi—have been associated with profound subjective alterations in awareness, self-processing, and perceptual integration. While anecdotal accounts often invoke metaphysical explanations, contemporary neuroscience provides measurable correlates of altered brain states including oscillatory synchronization, large-scale functional connectivity shifts, and modulation of metabolic and autonomic parameters.

This paper proposes a structured research framework for investigating High-Integration Brain States (HIBS) using electrophysiological, neuroimaging, and computational modeling approaches. We define HIBS operationally as neural regimes characterized by increased global coherence, altered network topology, and enhanced effective connectivity. We outline methodological pathways for empirical investigation, propose quantifiable metrics for information integration, and examine implications for cognitive enhancement and brain–computer interface (BCI) optimization. The framework excludes metaphysical claims and focuses exclusively on measurable neurobiological processes.

1. Introduction

1.1 Background

Contemplative traditions have long described advanced states of consciousness characterized by diminished ego-boundaries, enhanced perceptual clarity, and non-dual awareness. In modern neuroscience, such states are increasingly investigated under frameworks including:

Neural oscillatory synchronization
Large-scale network integration
Default Mode Network (DMN) modulation
Thalamocortical coupling dynamics
Predictive processing attenuation

Prior studies of experienced meditators have demonstrated:

Increased gamma-band coherence
Reduced DMN activity
Increased frontoparietal connectivity
Altered autonomic regulation

However, no unified mechanistic model currently exists that integrates these findings into a coherent systems-level explanation.

2. Conceptual Framework

2.1 Operational Definition

We define High-Integration Brain States (HIBS) as:

Transient or sustained neural regimes characterized by increased large-scale synchronization, enhanced effective connectivity across cortical networks, and measurable changes in metabolic and autonomic regulation.

This definition is strictly neurophysiological.

2.2 Rejection of Unsupported Hypotheses

The following claims lack empirical support and are excluded:

A global transition from “neurochemical” to “neuroelectric” synaptic functioning
Reversal of synaptic input-output geometry
Large-scale electromagnetic interaction with external fields
Neuronal phase transformations via unspecified “bioenergy”

All synaptic transmission remains electrochemical in nature. Brain oscillations represent coordinated electrical activity but do not replace synaptic chemistry.

3. Theoretical Basis

3.1 Oscillatory Coherence

Neural oscillations coordinate information processing. Gamma-band (30–100 Hz) coherence has been associated with:

Attention regulation
Memory binding
Conscious access

We hypothesize that HIBS involve elevated cross-regional phase synchronization.

Metric examples: $PLV = \left| \frac{1}{N} \sum_{k=1}^{N} e^{i(\phi_1(k)-\phi_2(k))} \right|$ PLV=N1k=1∑Nei(ϕ1(k)−ϕ2(k))

where PLV = Phase-Locking Value.

3.2 Network Topology

Using graph theory, brain networks can be described via:

Modularity (Q)
Global efficiency
Small-worldness
Rich-club connectivity

Hypothesis:

HIBS correspond to reduced modular segregation and increased global efficiency without pathological hyper-synchronization.

3.3 Information Integration

We propose investigation of effective information integration: $\Phi_{eff} = \text{Transfer entropy across distributed cortical subnetworks}$ Φeff=Transfer entropy across distributed cortical subnetworks

Alternative metrics:

Granger causality
Directed transfer function
Integrated information approximations
Dynamic causal modeling

HIBS may increase bidirectional effective connectivity rather than simply amplifying local oscillatory power.

3.4 Metabolic and Autonomic Coupling

Breath regulation and focused attention alter:

CO₂ concentration
Cerebral blood flow
Neurovascular coupling
Vagal tone (HRV indices)

These changes influence cortical excitability thresholds and oscillatory regimes.

4. Research Methodology

4.1 Experimental Design

Participants

Experienced contemplative practitioners (≥10,000 hours)
Matched control group

Modalities

High-density EEG (128–256 channels)
MEG (for phase synchronization)
fMRI (resting-state and task-based)
HRV monitoring
Breath gas analysis (CO₂, O₂)

4.2 Experimental Phases

Baseline resting state
Focused attention meditation
Open monitoring
Advanced absorption attempt
Post-state recovery

4.3 Data Analysis

Primary endpoints:

Gamma coherence change (Δγ coherence)
DMN suppression magnitude
Global efficiency increase
Transfer entropy increase
HRV modulation

Secondary endpoints:

Subjective phenomenology correlation mapping
Time-to-state induction variability

5. Brain–Computer Interface Implications

Higher neural coherence may improve:

Signal-to-noise ratio in EEG decoding
Stability of oscillatory control signals
Reduced latency in BCI command execution

Hypothesis: $Accuracy_{BCI} \propto Coherence_{global}$ AccuracyBCI∝Coherenceglobal

This relationship requires empirical validation.

6. Artificial Modulation (Non-Invasive Only)

Permissible experimental tools:

Transcranial alternating current stimulation (tACS)
Transcranial magnetic stimulation (TMS)
Closed-loop neurofeedback
Real-time coherence monitoring

All interventions must meet safety thresholds and IRB approval.

7. Ethical Considerations

No coercive cognitive enhancement
Informed consent required
Neural data privacy protection
Reversibility of stimulation protocols
Avoidance of germline genetic manipulation

Enhancement beyond therapeutic restoration requires international ethical consensus.

8. Discussion

HIBS may represent a neurophysiological regime characterized by:

Enhanced large-scale integration
Reduced self-referential dominance
Increased network coordination

These states do not imply metaphysical claims but reflect altered network dynamics.

Potential applications include:

Cognitive training
Mental health stabilization
BCI enhancement
AI-assisted cognition

Further research must separate subjective reports from measurable neural mechanisms.

9. Limitations

Correlation does not imply causation
Small sample sizes common in meditation studies
Risk of expectancy bias
Difficulty standardizing subjective depth

Longitudinal multi-site studies are required.

10. Conclusion

High-Integration Brain States can be investigated within a rigorous neuroscientific framework. By focusing on measurable oscillatory, network, and metabolic variables, contemplative-state research can be integrated into cognitive science without invoking unsupported claims.

The proposed framework establishes a reproducible research pathway for studying advanced meditative absorption and evaluating its relevance to cognitive augmentation and human–AI interface systems.

Below is a formal Mathematical Modeling section for network integration dynamics in High-Integration Brain States (HIBS). It is written in journal style, with explicit definitions, state equations, stability conditions, and measurable observables (EEG/MEG/fMRI). No metaphysical constructs; everything is operational and computable.

4. Mathematical Modeling of Network Integration Dynamics

4.1 Notation and Modeling Scope

Let the brain be represented as a time-varying directed weighted network $G(t)=(V,E,W(t)),$ G(t)=(V,E,W(t)),

where $V=\{1,\dots,N\}$ V={1,…,N} is the set of nodes (parcels/ROIs/sensors), $E\subseteq V\times V$ E⊆V×V edges, and $W(t)=[w_{ij}(t)]$ W(t)=[wij(t)] the effective (causal) coupling matrix inferred from data.

We distinguish:

Structural connectivity $S$ S (slow, anatomical; e.g., DTI)
Functional connectivity $F(t)$ F(t) (statistical dependence; e.g., correlation, coherence)
Effective connectivity $W(t)$ W(t) (directed causal influence; e.g., DCM, Granger, TE)

HIBS is modeled as a control-driven transition between regimes of network integration and segregation.

4.2 State Variables and Observables

Define a latent neural state vector $x(t)\in\mathbb{R}^N$ x(t)∈RN

representing coarse-grained population activity (e.g., band-limited amplitude envelope or firing-rate proxy).

Define an external control vector $u(t)\in\mathbb{R}^m$ u(t)∈Rm

representing experimental/behavioral modulators (breath pacing, attention load, neurofeedback target, stimulation).

Define measurable outputs $y(t) = Cx(t)+\eta(t),$ y(t)=Cx(t)+η(t),

where $C$ C maps latent activity to sensors (EEG/MEG channels or fMRI BOLD), and $\eta$ η is noise.

4.3 Linear Time-Varying Effective Connectivity Model (Baseline)

A minimal effective-connectivity model is: $\dot{x}(t)=A(t)x(t)+B u(t)+\xi(t),$ x˙(t)=A(t)x(t)+Bu(t)+ξ(t),

where:

$A(t)\in\mathbb{R}^{N\times N}$ A(t)∈RN×N is time-varying effective coupling
$B\in\mathbb{R}^{N\times m}$ B∈RN×m maps controls to network nodes
$\xi(t)$ ξ(t) is process noise (Gaussian or colored)

HIBS hypothesis (systems form): during HIBS, $A(t)$ A(t) shifts such that global integration metrics increase while maintaining stability (non-epileptiform).

4.4 Nonlinear Oscillatory Network Model (Phase-Coherence Regime)

Because HIBS is frequently associated with oscillatory synchronization, we introduce phase dynamics. Let each node have an instantaneous phase $\theta_i(t)$ θi(t) and (optionally) amplitude $r_i(t)$ ri(t).

4.4.1 Kuramoto–Sakaguchi Effective Model

$\dot{\theta}_i(t)=\omega_i + \sum_{j=1}^{N} K_{ij}(t)\sin(\theta_j-\theta_i-\alpha_{ij}) + \gamma_i^\top u(t)+\varepsilon_i(t)$ θ˙i(t)=ωi+j=1∑NKij(t)sin(θj−θi−αij)+γi⊤u(t)+εi(t)

$\omega_i$ ωi: intrinsic frequency
$K_{ij}(t)\ge0$ Kij(t)≥0: effective coupling strength
$\alpha_{ij}$ αij: phase-lag (captures delays/asymmetries)
$\gamma_i$ γi: control sensitivity

A global coherence order parameter: $R(t)e^{i\Psi(t)} = \frac{1}{N}\sum_{j=1}^{N} e^{i\theta_j(t)}$ R(t)eiΨ(t)=N1j=1∑Neiθj(t)

with $R(t)\in[0,1]$ R(t)∈[0,1]. HIBS corresponds to sustained elevation of $R(t)$ R(t) across relevant bands (e.g., alpha–gamma) without pathological hypersynchrony.

4.4.2 Amplitude–Phase (Stuart–Landau) Extension

$\dot{z}_i = (\lambda_i + i\omega_i – |z_i|^2)z_i + \sum_{j} W_{ij}(t) z_j + \Gamma_i^\top u(t) + \epsilon_i(t)$ z˙i=(λi+iωi−∣zi∣2)zi+j∑Wij(t)zj+Γi⊤u(t)+ϵi(t)

where $z_i=r_ie^{i\theta_i}$ zi=rieiθi is a complex oscillator state. Parameter $\lambda_i$ λi controls excitability; $W_{ij}$ Wij drives coupling.

This form is useful for modeling transitions in both amplitude and synchronization.

4.5 Integration–Segregation as a Dynamical Control Objective

Define an integration functional $\mathcal{I}(t)$ I(t) and segregation functional $\mathcal{S}(t)$ S(t) from $W(t)$ W(t) or $F(t)$ F(t).

A practical choice:

Global efficiency (integration proxy):

$E_{glob}(t)=\frac{1}{N(N-1)}\sum_{i\ne j}\frac{1}{d_{ij}(t)}$ Eglob(t)=N(N−1)1i=j∑dij(t)1

where $d_{ij}(t)$ dij(t) is shortest-path distance in weighted graph.

Modularity (segregation proxy):

$Q(t)=\frac{1}{2m}\sum_{ij}\left(w_{ij}(t)-\frac{k_i(t)k_j(t)}{2m}\right)\delta(g_i,g_j)$ Q(t)=2m1ij∑(wij(t)−2mki(t)kj(t))δ(gi,gj)

where $k_i=\sum_j w_{ij}$ ki=∑jwij, $m=\frac12\sum_{ij} w_{ij}$ m=21∑ijwij, and $g_i$ gi is community assignment.

HIBS is modeled as a controlled regime where $E_{glob}(t)$ Eglob(t) increases while $Q(t)$ Q(t) does not collapse to a fully connected trivial network (i.e., maintains functional specialization).

We formalize a target band: $E_{glob}(t)\ge E^\*, \quad Q_{min}\le Q(t)\le Q_{max}.$ Eglob(t)≥E\*,Qmin≤Q(t)≤Qmax.

4.6 Plastic Effective Connectivity Update Law (Learning/Training Dynamics)

To model how training (meditation/neurofeedback) changes connectivity, define an adaptation law: $\dot{W}(t)= -\kappa W(t) + \mu\,\Phi(x(t)) + \sum_{k=1}^{m}\nu_k\,u_k(t)\,G_k$ W˙(t)=−κW(t)+μΦ(x(t))+k=1∑mνkuk(t)Gk

$\kappa>0$ κ>0: decay/regularization (prevents runaway coupling)
$\Phi(x)$ Φ(x): activity-dependent plasticity term (Hebbian-like)
$G_k$ Gk: control-specific coupling templates (which edges a control influences)

A concrete Hebbian form: $\Phi_{ij}(x)=x_i x_j – \rho w_{ij}$ Φij(x)=xixj−ρwij

or in oscillatory space: $\Phi_{ij}(\theta)=\cos(\theta_i-\theta_j) – \rho w_{ij}.$ Φij(θ)=cos(θi−θj)−ρwij.

This explicitly predicts that sustained coherent practice increases coupling among task-relevant networks but is stabilized by decay and regularization.

4.7 Stability and Non-Pathological Constraints

HIBS must be separated from pathological hypersynchrony (seizure-like). We enforce stability constraints on dynamics.

4.7.1 Linear Model Stability

For $\dot{x}=A(t)x$ x˙=A(t)x, sufficient condition: $\lambda_{\max}\left(\frac{A(t)+A(t)^\top}{2}\right) \le -\epsilon < 0$ λmax(2A(t)+A(t)⊤)≤−ϵ<0

for all $t$ t in the modeled window.

Interpretation: the symmetric part of $A(t)$ A(t) must be negative definite enough to avoid explosive growth.

4.7.2 Oscillator Model Safety

For Kuramoto-type networks, avoid full-locking across all nodes at high coupling. Practical constraint: $\|K(t)\| \le K_{safe}$ ∥K(t)∥≤Ksafe

and verify spectral spread remains bounded (no global collapse into single attractor).

In practice, impose:

coherence increases in selected subnetworks/bands
whole-brain $R(t)$ R(t) does not saturate to 1 across all bands

4.8 Linking Model Quantities to Empirical Metrics

The model yields explicit mapping to measurable quantities.

EEG/MEG coherence: estimated phase-locking or imaginary coherence approximates coupling effects of $K_{ij}(t)$ Kij(t).
fMRI functional connectivity: correlation of amplitude envelopes approximates low-frequency components of $F(t)$ F(t).
Effective connectivity: infer $W(t)$ W(t) via:

Dynamic causal modeling (DCM)
Time-varying Granger causality
Transfer entropy (TE)

Transfer entropy from $j\to i$ j→i: $TE_{j\to i} = \sum p(x_i^{t+1},x_i^t,x_j^t)\log\frac{p(x_i^{t+1}\mid x_i^t,x_j^t)}{p(x_i^{t+1}\mid x_i^t)}$ TEj→i=∑p(xit+1,xit,xjt)logp(xit+1∣xit)p(xit+1∣xit,xjt)

HIBS prediction: $TE$ TE increases for cross-network channels (e.g., frontoparietal $\leftrightarrow$ ↔ salience) and decreases for DMN self-referential loops if DMN suppression occurs.

4.9 Regime Switching: A Formal HIBS Transition Model

We model HIBS induction as a regime switch with a latent discrete state $s(t)\in\{0,1\}$ s(t)∈{0,1}:

$s(t)=0$ s(t)=0: baseline
$s(t)=1$ s(t)=1: HIBS

$\dot{x}(t)=A_{s(t)}x(t)+B u(t)+\xi(t)$ x˙(t)=As(t)x(t)+Bu(t)+ξ(t)

and a control-dependent switching probability (continuous-time hazard): $\Pr(s:0\to1 \text{ in } [t,t+\Delta t]) \approx h(u(t),\chi(t))\Delta t$ Pr(s:0→1 in [t,t+Δt])≈h(u(t),χ(t))Δt

where $\chi(t)$ χ(t) may include autonomic state (HRV, breath rate), fatigue, or prior practice dose.

A practical logistic form: $h = \sigma\big(a_0 + a^\top u(t) + b^\top \chi(t)\big).$ h=σ(a0+a⊤u(t)+b⊤χ(t)).

This supports data-driven estimation of “induction likelihood” as a function of training variables.

4.10 Control Objective for Neurofeedback/Training Protocols

Define a cost functional for training: $J=\int_0^T \left[\alpha\,(E^\*-E_{glob}(t))_+^2 + \beta\,(Q(t)-Q^\*)^2 + \gamma\,\|u(t)\|^2 \right]dt$ J=∫0T[α(E\*−Eglob(t))+2+β(Q(t)−Q\*)2+γ∥u(t)∥2]dt

subject to stability constraints in §4.7.

This formalizes training as optimal control: maximize integration to a target window while maintaining specialization and safety.

4.11 Model Deliverables and Testable Predictions

Deliverable 1: Parameterized $A(t)$ A(t), $K(t)$ K(t), or $W(t)$ W(t) fitted from multi-modal recordings.
Deliverable 2: Quantitative “HIBS index”: $\mathcal{H}(t)= w_1 R(t) + w_2 E_{glob}(t) – w_3 \text{DMN}(t) – w_4 \text{Instability}(t)$ H(t)=w1R(t)+w2Eglob(t)−w3DMN(t)−w4Instability(t)

with weights learned from labeled sessions.

Predictions (falsifiable):

HIBS corresponds to increased cross-network effective connectivity $W(t)$ W(t) with bounded stability.
Training dose predicts drift in plasticity update law parameters ( $\mu,\kappa$ μ,κ).
Autonomic coherence (HRV increase, breath regularity) predicts higher transition hazard $h$ h.