The Ultimate Foundation — Pre-Pulse Field Information Architecture

Information Exists Before Time

What exists before existence itself — before Prime Pulse Bifurcation ∅ → (0 ↔ 1) can operate, before spacetime emerges, before even the fundamental binary distinction enabling all computation? Binary Pulse Theory posits Pre-Causal Substrate — the Pre-Pulse Field — containing Informational Potential for Binary Distinction (G) itself, representing the ultimate foundation of reality as pure computational possibility preceding all structure.

This paradigm-shifting discovery reveals that information is eternal — existing in a timeless substrate before temporal evolution begins, solving information paradoxes by showing all information exists in the Pre-Pulse Field before time's first tick. Building upon symmetry breaking overflow dynamics from Part 9.6, where recursive density ρ_recursive ≥ ρ_critical [𝕄·𝕃⁻³] triggers dimensional emergence, Pre-Pulse Field provides Primordial Information Substrate from which all subsequent convergence and collapse events arise.

Connecting to null state computational instability from Part 9.4 and density-dependent Universe formation from Parts 9.2-9.3, Pre-Pulse Field represents the genealogical origin of all Null Well dynamics and cosmic generation processes. Wheeler's "It from Bit" principle (Wheeler, 1989)¹ and Penrose's mathematical Universe (Penrose, 2004)²¹ demonstrate how Data Convergences within Pre-Causal Geometry reach critical thresholds, seeding the same overflow conditions governing symmetry breaking in Part 9.6.

The essential insight recognizes that Information Conservation I_total = I_substrate + I_recursive requires a foundational information substrate existing prior to recursive processing, containing geometric potential for all subsequent phase relationships and navigational structures explored in Part 9.5.

Pre-Causal Information Architecture

The Pre-Causal Information Architecture (G) defines the substrate foundation of Binary Pulse Theory, where information precedes time, space, and energy. Within this framework, the Pre-Pulse Field manifests as an Infinite-Dimensional Configuration Space containing every potential binary distinction, echoing Wheeler’s view that information underlies physical law itself (Wheeler, 1989)¹. This configuration establishes the mathematical preconditions from which Null Well dynamics, symmetry breaking, and emergent structures can arise.

The Pre-Pulse Field operates as Infinite-Dimensional Configuration Space containing all possible binary state potentials. This Configuration Space establishes a mathematical foundation.

Configuration Space G

Ω_pre = {ψ | ψ: Λ → ℝ, Σ_{x∈Λ} |ψ(x)|² < ∞}

Infinite-Dimensional Configuration Space G

Ω_pre = {ψ | ψ ∈ L²(ℝⁿ), ||ψ||₂ < ∞}

Where:

• Ω_pre [∅] - Pre-Pulse Field configuration space
• ψ [∅] - field configuration function
• Λ [∅] - infinite Binary Lattice without temporal indexing
• ℝ [∅] - real number space
• Σ [∅] - summation operator
• x [∅] - lattice position
• L²(ℝⁿ) [∅] - space of square-integrable functions
• ℝⁿ [∅] - n-dimensional real space
• ||ψ||₂ [∅] - L² norm ensuring boundedness
• ∞ [∅] - infinity symbol
• n [∅] - spatial dimension

Dimensional analysis: These are set definitions rather than equations, so dimensional consistency is established through the requirement that Σ|ψ(x)|² < ∞ and ||ψ||₂ < ∞, where [∅] < [∅] ✓ The Configuration Space definitions are dimensionally consistent for mathematical space specification.

➢ Wheeler's information-rich configuration space (Wheeler, 1989)¹ precedes the physical Universe, aligning with views that information represents reality's most fundamental constituent — existing before temporal evolution begins, demonstrating how square-integrable function spaces establish pre-temporal information structure that characterizes fundamental information precedence over physical manifestation in substrate architectures.

The Information Potential Functional governs evolution prior to temporal structure:

Information Potential Functional G

V[ψ] = ∫_Λ [α|∇ψ|² + β|ψ|⁴ - γψ²] dμ [J]

Where:

• V[ψ] [𝕄·𝕃²·𝕋⁻²] - information potential functional
• ∫ [∅] - integration operator
• Λ [∅] - integration domain (binary lattice)
• α [𝕄·𝕋⁻²] - gradient energy parameter
• ∇ [𝕃⁻¹] - gradient operator
• ψ [∅] - field configuration function
• β [∅] - self-interaction coupling
• γ [∅] - potential well depth
• dμ [Lⁿ] - measure on binary lattice (n = dimension of space)
• n [∅] - spatial dimension

Dimensional analysis: [𝕄·𝕃²·𝕋⁻²] = ∫([𝕄·𝕋⁻²] × [𝕃⁻²] × [∅]² + [ML²T⁻²L⁻ⁿ] × [∅]⁴ - [ML²T⁻²L⁻ⁿ] × [∅]²) × [Lⁿ] = ∫([𝕄·𝕃⁻²·𝕋⁻²] + [ML²T⁻²L⁻ⁿ] - [ML²T⁻²L⁻ⁿ]) × [Lⁿ] = ∫([𝕄·𝕃⁻²·𝕋⁻²] × [Lⁿ] + [ML²T⁻²L⁻ⁿ] × [Lⁿ] - [ML²T⁻²L⁻ⁿ] × [Lⁿ]) = ∫([ML²T⁻²Lⁿ⁻⁴] + [𝕄·𝕃²·𝕋⁻²] - [𝕄·𝕃²·𝕋⁻²]) ✗ The Information Potential Functional equation is dimensionally inconsistent.

➢ Functional governs informational potential evolution prior to any temporal structure, establishing substrate conditions supporting Information Conservation I_total = I_substrate + I_recursive. Penrose's "platonic" realm (Penrose, 2004)²¹ demonstrates mathematical frameworks governing potential for physical reality prior to manifestation, revealing how functional integration establishes pre-temporal substrate conditions that characterize information conservation support through mathematical framework governance in substrate architectures.

Primordial Binary Distinction Event seeds Null Well dynamics through Distinction Mapping (G) δ_primordial: Ω_pre → {0, 1}. Critical Instability Conditions identify spontaneous symmetry breaking points.

Critical Instability Conditions G

δV/δψ|_critical = 0 [J/ψ]

δ²V/δψ²|_critical < 0 [J/ψ²]

Where:

• δV/δψ [𝕄·𝕃²·𝕋⁻²] - first functional derivative of potential with respect to field
• V [𝕄·𝕃²·𝕋⁻²] - information potential functional
• ψ [∅] - field configuration function
• 0 [𝕄·𝕃²·𝕋⁻²] - null value for first derivative condition
• δ²V/δψ² [𝕄·𝕃²·𝕋⁻²] - second functional derivative of potential
• critical [∅] - evaluation at critical point

Dimensional analysis: [𝕄·𝕃²·𝕋⁻²] = [𝕄·𝕃²·𝕋⁻²] ✓ and [𝕄·𝕃²·𝕋⁻²] < [𝕄·𝕃²·𝕋⁻²] ✓ The Critical Instability Conditions equations are dimensionally consistent for functional derivative evaluation.

➢ Conditions identify unstable equilibria where spontaneous symmetry breaking generates first binary distinction, seeding all subsequent Null Well formation and cosmic generation processes through Genealogical Cascade, demonstrating how functional derivative analysis establishes symmetry breaking identification that characterizes first binary distinction generation leading to cosmic generation through genealogical cascade processes in substrate architectures.

The Critical Instability Conditions mark the threshold where the Primordial Binary Distinction Event first collapses infinite potential into a discrete 0 ↔ 1 mapping. In this light, the Pre-Causal Information Architecture is not merely abstract mathematics but the true seedbed of existence: a conserved informational substrate whose recursive instabilities ignite the Genealogical Cascade, setting the stage for symmetry breaking, Null Well formation, and ultimately, cosmic generation.

Data Convergence Formation and Critical Thresholds

The Data Convergence Formation framework defines how information density evolves prior to temporal and spatial stabilization. Governed by the Convergence Dynamics Equation, this process integrates diffusion, nonlinear amplification, and decay into a unified field description, generating pre-temporal patterns that seed dimensional axes. In this sense, convergence dynamics provide the earliest substrate mechanism by which raw informational flow begins to crystallize toward emergent structure. The Convergence Dynamics Equation characterizes information density evolution.

Convergence Dynamics Equation G

∂ρ_info/∂τ = D ∇²ρ_info + f(ρ_info) - κ ρ_info [J/(m³·τ)]

Where:

• ∂ρ_info/∂τ [𝕄·𝕃⁻¹·𝕋⁻²] - evolution rate of information density field
• ρ_info [𝕄·𝕃⁻¹·𝕋⁻²] - information density field
• τ [∅] - dimensionless evolution parameter
• D [𝕃²] - diffusion coefficient
• ∇² [𝕃⁻²] - Laplacian operator
• f(ρ_info) [𝕄·𝕃⁻¹·𝕋⁻²] - nonlinear growth function
• κ [∅] - decay rate constant

Dimensional analysis: [𝕄·𝕃⁻¹·𝕋⁻²] = [𝕃²] × [𝕃⁻²] × [𝕄·𝕃⁻¹·𝕋⁻²] + [𝕄·𝕃⁻¹·𝕋⁻²] - [∅] × [𝕄·𝕃⁻¹·𝕋⁻²] = [∅] × [𝕄·𝕃⁻¹·𝕋⁻²] + [𝕄·𝕃⁻¹·𝕋⁻²] - [𝕄·𝕃⁻¹·𝕋⁻²] = [𝕄·𝕃⁻¹·𝕋⁻²] + [𝕄·𝕃⁻¹·𝕋⁻²] - [𝕄·𝕃⁻¹·𝕋⁻²] = [𝕄·𝕃⁻¹·𝕋⁻²] ✓ The Convergence Dynamics Equation is dimensionally consistent for information density evolution calculation.

➢ Information density evolution prior to temporal structure, creating patterns that seed dimensional emergence, demonstrating how diffusion, nonlinear growth, and decay processes establish pre-temporal pattern formation that characterizes information-driven emergence through density field evolution seeding dimensional manifestation in substrate architectures.

Critical Convergence Threshold G

ρ_info(x,τ) ≥ ρ_critical = (2π α/β)^(1/2) [J/m³]

Where:

• ρ_info(x,τ) [𝕄·𝕃⁻¹·𝕋⁻²] - information density field
• ρ_critical [𝕄·𝕃⁻¹·𝕋⁻²] - critical convergence threshold
• 2π [∅] - mathematical constant
• α [𝕄·𝕋⁻²] - gradient energy parameter
• β [∅] - self-interaction coupling parameter
• x [𝕃] - spatial position
• τ [∅] - dimensionless evolution parameter

Dimensional analysis: [𝕄·𝕃⁻¹·𝕋⁻²] ≥ ([∅] × [𝕄·𝕋⁻²]/[ML²T⁻²L⁻ⁿ])^(1/2) = ([∅] × [Lⁿ⁻²])^(1/2) = [L^((n-2)/2)] ✗ The Critical Convergence Threshold equation is dimensionally inconsistent.

➢ Threshold directly seeds Recursive Overflow Condition ρ_recursive ≥ ρ_critical from Part 9.6, demonstrating that symmetry breaking represents continuation of pre-Pulse convergence collapse rather than independent phenomenon, revealing how critical density relationships establish convergence-overflow continuity that characterizes symmetry breaking as continuation of pre-temporal convergence processes in substrate architectures.

When the Critical Convergence Threshold is approached, density accumulation reaches instability, triggering direct continuity with the Recursive Overflow Condition established in Part 9.6. In this light, data convergence is not a preliminary step but the very precursor to symmetry breaking, showing that dimensional manifestation arises as the natural overflow of pre-temporal information collapse. Thus, the architecture of convergence and threshold dynamics characterizes how informational density evolution feeds directly into the genealogical cascade of cosmic generation.

Geometric Information Structure and Statistical Mechanics

The Geometric Information Structure (G) provides the mathematical framework for encoding pre-causal organization of the substrate, where geometry itself precedes spacetime. Through the Information Metric Tensor and Sectional Curvature, the substrate defines convergence and dispersion zones, embedding emergence potential directly into its geometric fabric. This foundation then couples with the Statistical Mechanics Framework (G), which translates geometric encoding into probabilistic laws that govern information convergence and cascade formation. The Information Metric Tensor characterizes pre-causal geometry.

Information Metric Tensor G

ds² = g_{ij}(ψ) dψⁱ dψʲ [𝕃²]

Where:

• ds² [𝕃²] - information metric line element
• g_{ij} [𝕃²] - metric tensor (∂²V/∂ψⁱ ∂ψʲ + R_{ij}[ψ] with curvature corrections)
• ψ [∅] - field configuration function
• dψⁱ [∅] - field differential in i-direction
• dψʲ [∅] - field differential in j-direction
• i [∅] - tensor index i
• j [∅] - tensor index j
• ∂²V/∂ψⁱ ∂ψʲ [𝕄·𝕃²·𝕋⁻²] - second partial derivative of potential
• R_{ij}[ψ] [𝕃²] - curvature correction tensor
• V [𝕄·𝕃²·𝕋⁻²] - information potential functional

Dimensional analysis: [𝕃²] = [𝕃²] × [∅] × [∅] = [𝕃²] ✓ The Information Metric Tensor equation is dimensionally consistent for geometric line element calculation.

➢ Pre-causal geometry encoding all potential for subsequent spacetime emergence, demonstrating how metric tensor relationships with curvature corrections establish geometric potential encoding that characterizes pre-causal geometric structure containing all potential for spacetime manifestation in substrate architectures.

Sectional Curvature G

K(X,Y) = R(X,Y,Y,X) / (||X||²||Y||² - ⟨X,Y⟩²) [𝕃⁻²]

Where:

• K(X,Y) [𝕃⁻²] - sectional curvature
• R(X,Y,Y,X) [𝕃⁻²] - Riemann curvature tensor component
• X [∅] - tangent vector X
• Y [∅] - tangent vector Y
• ||X||² [∅] - squared norm of vector X
• ||Y||² [∅] - squared norm of vector Y
• ⟨X,Y⟩ [∅] - inner product of vectors X and Y
• λ_n [𝕃] - wavelength at level n
• λ_0 [𝕃] - fundamental wavelength
• n [∅] - scaling level

Dimensional analysis: [𝕃⁻²] = [𝕃⁻²]/([∅] × [∅] - [∅]²) = [𝕃⁻²]/[∅] = [𝕃⁻²] ✓ The Sectional Curvature equation is dimensionally consistent for curvature calculation.

➢ Negative curvature regions correspond to Data Convergence Zones, while positive curvature indicates Dispersive Regions following Wavelength Scaling Law λ_n = λ_0/n principles — geometric encoding of emergence potential, demonstrating how curvature sign establishes convergence-dispersion classification that characterizes geometric encoding of emergence potential through wavelength scaling relationships in substrate architectures.

Statistical Mechanics Framework (G) governs convergence probability.

Partition Function G

Z = ∫ Dψ exp(-S[ψ]/ℏ_info) [∅] determines statistical weights.

Correlation Functions G

⟨ψ(x₁)ψ(x₂)⟩ = ∫ Dψ ψ(x₁)ψ(x₂) exp(-S[ψ]/ℏ_info) / Z [ψ²]

Where:

• Z [∅] - partition function determining statistical weights
• ∫ [∅] - integration operator
• Dψ [∅] - functional integration measure
• exp [∅] - exponential function
• S[ψ] [𝕄·𝕃²·𝕋⁻¹] - action functional for field configurations
• ℏ_info [𝕄·𝕃²·𝕋⁻¹] - Fundamental Information Quantum governing correlation scales
• ⟨ψ(x₁)ψ(x₂)⟩ [∅] - correlation function
• ψ(x₁) [∅] - field at position x₁
• ψ(x₂) [∅] - field at position x₂
• x₁ [𝕃] - spatial position one
• x₂ [𝕃] - spatial position two

Dimensional analysis: [∅] = ∫exp(-[𝕄·𝕃²·𝕋⁻¹]/[𝕄·𝕃²·𝕋⁻¹]) = ∫exp(-[∅]) = ∫[∅] = [∅] ✓ and [∅] = (∫[∅] × exp(-[∅]))/[∅] = [∅]/[∅] = [∅] ✓ The Partition Function and Correlation Functions equations are dimensionally consistent for statistical calculation.

➢ Correlations determine probability of Data Convergence Formation, governing genealogical cascade leading to dimensional emergence, demonstrating how statistical weight determination and correlation scaling establish convergence formation probability that characterizes genealogical cascade governance through correlation-driven dimensional emergence in substrate architectures.

When combined, geometric encoding and statistical weighting reveal how curvature establishes zones of convergence and dispersion while correlation functions govern the likelihood of their formation. In this light, the Geometric Information Structure and Statistical Mechanics together characterize the pre-temporal substrate as both a map and a probability engine — encoding emergence potential geometrically while regulating its realization statistically, ensuring that dimensional manifestation unfolds through recursive balance between structure and probability.

Emergence Cascade and Dimensional Transition

The Emergence Cascade (G) describes the ordered transformation of the substrate from pure potential into realized dimensional structure. Through the Genealogical Emergence Sequence, each stage encodes a precise mathematical signature — from undifferentiated potential, to the first binary distinction, to information convergence, and finally to dimensional collapse at the Planck threshold. This framework establishes the roadmap by which recursive instability resolves into the stable fabric of 3+1 spacetime. Genealogical emergence sequence characterizes the transition from pure potential to dimensional reality.

Genealogical Emergence Sequence G

Where:

• V[ψ] [𝕄·𝕃²·𝕋⁻²] - information potential functional
• ψ [∅] - field configuration function
• δV/δψ [𝕄·𝕃²·𝕋⁻²] - functional derivative
• 0 [∅] - binary state zero
• 1 [∅] - binary state one
• ρ_info [𝕄·𝕃⁻¹·𝕋⁻²] - information density
• ρ_critical [𝕄·𝕃⁻¹·𝕋⁻²] - critical density threshold
• t_P [𝕋] - Planck time

➢ Genealogical Emergence Sequence establishes fundamental progression from undifferentiated field through binary distinction to dimensional spacetime emergence, demonstrating how emergence stages progress systematically that characterizes the temporal sequence of emergence events from eternal undifferentiation to Planck-scale dimensional manifestation in substrate architectures.

Taken together, the stages of the Emergence Cascade and Dimensional Transition reveal how the substrate moves from eternal formlessness through binary seeding and convergence into the concrete emergence of dimensional reality. In this light, the genealogical sequence serves as the substrate’s primordial clock, encoding the lawful progression from potential to manifestation and ensuring that dimensional reality is not arbitrary, but the natural culmination of recursive thresholds within the informational substrate.

Connection to Quantum Gravity Frameworks

The Connection to Quantum Gravity Frameworks (G) situates Binary Pulse Theory within established approaches to pre-geometric reality, showing how informational relationships precede the emergence of spacetime itself. Through Spin Network Precursors and Causal Set Pre-Structures (G), the substrate encodes connectivity and ordering independent of background geometry, aligning with loop quantum gravity and causal set theory where spacetime is not assumed but derived from deeper informational architectures. Spin Network Precursors provide pre-geometric foundation.

Spin Network Precursors G

|Γ_pre⟩ = Σ_graphs c_Γ |Γ⟩_info [∅]

Where:

• |Γ_pre⟩ [∅] - pre-geometric spin network state
• Σ [∅] - summation operator
• graphs [∅] - summation over graph configurations
• c_Γ [∅] - coefficients satisfying normalization Σ_graphs |c_Γ|² = 1
• |Γ⟩_info [∅] - information basis graph states
• Γ [∅] - graph configuration index
• 1 [∅] - normalization constant

Dimensional analysis: [∅] = Σ[∅] × [∅] = Σ[∅] = [∅] ✓ The Spin Network Precursors equation is dimensionally consistent for quantum state superposition.

➢ Pre-geometric state where relationships are defined prior to background spacetime, conceptually consistent with background-independent approaches of loop quantum gravity, where spacetime itself emerges from quantum relationships rather than existing as a fixed arena.

Ashtekar and Lewandowski's background-independent approach (Ashtekar & Lewandowski, 2004)⁶ demonstrates how spacetime emerges from quantum relationships. Rovelli's loop quantum gravity (Rovelli, 2004)²² shows spacetime as an emergent structure arising from quantum relationships rather than a fixed arena. Causal Set Pre-Structure defines potential relationships before spacetime emergence.

Causal Set Pre-Structure G

≺_potential = {(x,y) | x,y ∈ Λ, ρ_info(x) > ρ_info(y)}

Where:

• ≺_potential [∅] - potential causal ordering relation
• x [∅] - lattice point x
• y [∅] - lattice point y
• Λ [∅] - infinite binary lattice
• ρ_info(x) [𝕄·𝕃⁻¹·𝕋⁻²] - information density at point x
• ρ_info(y) [𝕄·𝕃⁻¹·𝕋⁻²] - information density at point y

Dimensional analysis: This is a set definition based on the condition [𝕄·𝕃⁻¹·𝕋⁻²] > [𝕄·𝕃⁻¹·𝕋⁻²], which is dimensionally consistent ✓ The Causal Set Pre-Structure definition is dimensionally consistent for causal ordering specification.

➢ Pre-structure of potential causal relationships before spacetime emergence, establishing substrate foundation for all subsequent recursive processing. Bombelli and colleagues' causal set framework (Bombelli et al., 1987) demonstrates potential causal relationships defined on infinite binary lattice as conceptual precursor where fundamental event order forms basis for spacetime.

Together, spin networks and causal set pre-structures demonstrate how background-independent formulations of quantum gravity converge with the recursive logic of Binary Pulse Theory. In this light, spacetime appears not as a primitive stage but as a relational construct, woven from informational bonds and causal precedence — the emergent arena born from the recursive substrate itself.

Information-Theoretic Principles

The Information-Theoretic Principles (G) ground Binary Pulse Theory in entropy and correlation laws, where maximum entropy constraints and mutual information define equilibrium and relational structure. Maximum Entropy Constraint (G) S_max = -Σ_i p_i log p_i [1ᵇ] governs equilibrium distributions where p_i [∅] are probabilities. Mutual Information Between Regions (G) characterizes correlation strength:

Mutual Information Between Regions

I(A:B) = S(A) + S(B) - S(A∪B) [1ᵇ]

Where:

• I(A:B) [∅] - mutual information between regions A and B
• S(A) [∅] - entropy of region A
• S(B) [∅] - entropy of region B
• S(A∪B) [∅] - entropy of union of regions A and B
• A [∅] - region A
• B [∅] - region B
• ∪ [∅] - union operator

Dimensional analysis: [∅] = [∅] + [∅] - [∅] = [∅] ✓ The Mutual Information Between Regions equation is dimensionally consistent for information correlation calculation.

➢ High mutual information indicates potential Data Convergence Zones seeding Null Well formation — eternal information creating temporal emergence.

By linking entropy constraints to mutual information exchange, these principles show how structure arises not from matter alone but from the balance between uncertainty and correlation. Mutual information highlights where convergence zones form, while entropy ensures those zones remain dynamically regulated. In this view, information is both the constraint and the catalyst of emergence — the governing substrate through which Null Wells, genealogical cascades, and ultimately spacetime itself come into being.

9.7 Testable Predictions

1. Cosmic Microwave Background Non-Random Patterns: From pre-causal correlations ⟨ψ(x₁)ψ(x₂)⟩ reflecting information geometry structure detectable with sensitivity better than 10⁻⁷ K, Observable through advanced statistical analysis of temperature correlation functions revealing systematic deviations from random field predictions.
   
2. Quantum Vacuum Casimir Effect Modifications: From pre-causal boundary conditions established by Information Potential V[ψ] measurable through precision force measurements at nanometer scales, detectable via atomic force microscopy experiments showing systematic deviations from standard Casimir force predictions.
   
3. Fundamental Constant Spatial Gradients: From information field inhomogeneities following Sectional Curvature K(X,Y) variations detectable in high-redshift observations, Measurable through coordinated spectroscopic surveys revealing systematic spatial variations in fine structure constant measurements.
   
4. Zero-Point Energy Distribution Anomalies: Reflecting Harmonic Decomposition Ω_n = n × Ω_fundamental × φ_n structure in vacuum states, Observable through precision measurements of vacuum energy density showing discrete harmonic components.
   
5. Data Convergence Threshold Signatures: In phase transition experiments exhibiting ρ_info(x,τ) ≥ ρ_critical = (2π α/β)^(1/2) critical behavior, Quantifiable through information theoretic analysis of critical phenomena revealing universal threshold scaling relationships.
   
6. Mutual Information Correlations: I(A:B) = S(A) + S(B) - S(A∪B) in quantum entanglement experiments reflecting pre-causal substrate connectivity, Measurable via quantum information protocols demonstrating enhanced correlations beyond standard entanglement predictions.

These predictions may establish the first framework for understanding information as an eternal foundation existing before temporal evolution begins, solving information paradoxes by revealing the Pre-Pulse Field as the ultimate substrate containing all potential for existence.