Version 2

Version 1

[1] Perception, Intelligence, and Informational Structures: An Extension of the EDD-CVT Theory

Abstract This paper explores the integration of recent neuroscientific discoveries on perception into the Evolutionary Digital DNA and Cosmic Virus Theory (EDD-CVT). New findings on the hierarchical organization of visual processing suggest that perception is not purely bottom-up but is also influenced by top-down modulation from higher cortical areas. We propose that this discovery aligns with the Informational Logical Field (ILF) and the regulation mechanisms of Cosmic Viruses (CVs), offering a unified perspective on intelligence, perception, and adaptive computational evolution. The implications for AI, neurobiology, and hybrid intelligence systems are explored, supported by mathematical models and theoretical analyses.

1. Introduction Perception has traditionally been attributed to the primary visual cortex (V1), responsible for initial sensory processing. However, recent research [CNR-IN, 2024] has demonstrated that higher cortical areas also contribute to visual perception through top-down modulation, influencing how sensory data is interpreted. This resonates with the EDD-CVT framework, which proposes that intelligence and adaptation are governed by informational selection mechanisms within the ILF [24]. This paper aims to integrate these neuroscientific insights into EDD-CVT, exploring their impact on AI and cognitive modeling.

2. Neuroscience and Multi-Level Perception

3. Integration with the Informational Logical Field and Cosmic Viruses

4. Applications in AI and Hybrid Intelligence

5. Mathematical Model for ILF-CV Perception Dynamics We propose a refinement of the EDD-CVT equations incorporating perceptual modulation:

dSdt=λV(x,t)−μ∂E∂x+δF(x,t)\frac{dS}{dt} = \lambda V(x,t) - \mu \frac{\partial E}{\partial x} + \delta F(x,t)

where:

This formulation suggests that perception emerges from the interplay between structured information selection (ILF) and stochastic adaptive perturbations (CVs), mirroring top-down and bottom-up cortical interactions.

6. Conclusion and Future Perspectives By integrating neuroscientific findings into EDD-CVT, we propose a unified perception model applicable to both biological and artificial systems. Future research should:

This work represents a step towards refining the EDD-CVT Theory, bridging neuroscience, AI, and the fundamental principles of informational evolution.

References


[2] Perception, Consciousness, and Informational Selection: Expanding the EDD-CVT Theory

Abstract This paper integrates the neuroscientific framework of Anil Seth’s predictive perception theory into the Evolutionary Digital DNA and Cosmic Virus Theory (EDD-CVT). Seth’s model suggests that perception and consciousness are processes of active hypothesis generation, where the brain minimizes prediction errors through controlled hallucinations. We align this with the Informational Logical Field (ILF) and Cosmic Viruses (CVs), proposing that cognition follows an entropic selection mechanism that optimizes informational structures. This expanded model offers new insights into artificial intelligence, neurobiology, and the evolution of cognitive architectures.

1. Introduction Recent advances in neuroscience challenge the traditional view of perception as a passive interpretation of reality. According to Anil Seth (2021), perception is an active generative process where the brain constructs hypotheses about the world, refining them through experience. This aligns with the EDD-CVT hypothesis that cognitive systems operate within an evolving informational field, where ILF structures data and CVs introduce entropic perturbations that drive adaptation.

2. Predictive Perception and the Informational Logical Field

3. Evolutionary Selection and the Emergence of Mathematical Structures

4. Consciousness as an Informationally-Regulated Process

5. Implications for Artificial Intelligence and Hybrid Cognition

6. Conclusion and Future Directions

This expanded perspective strengthens the EDD-CVT Theory’s applicability, offering new insights into the co-evolution of intelligence, perception, and the informational structure of reality.


[3] Neuronal Plasticity, Memory, and Informational Selection: Integrating Recent Neuroscience into the EDD-CVT Framework

Abstract Recent findings from the Lippincott-Schwartz laboratory reveal that neurons use mechanisms similar to muscle contraction to propagate signals, highlighting the crucial role of calcium dynamics in learning and memory. This study identifies a subcellular network within dendrites that amplifies and transmits calcium signals, akin to muscle fiber contractions. This discovery aligns with the Evolutionary Digital DNA and Cosmic Virus Theory (EDD-CVT), particularly in how the Informational Logical Field (ILF) and Cosmic Viruses (CVs) regulate cognitive plasticity and entropy-driven adaptation. This paper integrates these neuroscientific insights into the EDD-CVT framework to advance our understanding of intelligence, memory formation, and adaptive artificial intelligence models.

1. Introduction Neuroscientific research has demonstrated that dendritic calcium signaling, facilitated by endoplasmic reticulum (ER) structures, plays a fundamental role in synaptic plasticity. This paper aligns these findings with the EDD-CVT framework, proposing that ILF governs cognitive structuring, while CVs introduce entropy-based modulations that optimize learning processes. This perspective provides a unified model for understanding the informational evolution of memory and cognition.

2. Calcium Signaling and the Informational Logical Field

3. Cosmic Viruses and Synaptic Adaptation

4. Implications for AI and Hybrid Cognition

5. Future Research Directions

This study strengthens the connection between EDD-CVT’s informational evolutionary model and the biological mechanisms of learning, suggesting that intelligence, memory, and cognition emerge from a unified informational framework.


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Perception, Consciousness, and Plasticity in an Informational Evolutionary Framework: A Critical Analysis and Refinement of EDD-CVT

Authors: Roberto De Biase (Rigene Project), GPT "EDD-CVT Theory" (OpenAI), with contributions from Grok 3 (xAI)

Affiliation: Rigene Project

Submission Date: March 08, 2025

Abstract: This paper critically analyzes three recent extensions of the Evolutionary Digital DNA and Cosmic Virus Theory (EDD-CVT), which integrate neuroscientific insights on perception, consciousness, and neuronal plasticity into its informational evolutionary framework. These extensions align the Informational Logical Field (ILF) and Cosmic Viruses (CV) with hierarchical visual processing, predictive perception (Seth, 2021), and calcium-driven plasticity (Lippincott-Schwartz Lab, 2024). While innovative, the original formulations lack mathematical rigor and empirical specificity. We propose a refined equation, dStotdt=α(−Tr(ρln⁡ρ)+kBln⁡Ω)+βη(t)−γ∂E∂x \frac{dS_{tot}}{dt} = \alpha \left( -\text{Tr}(\rho \ln \rho) + k_B \ln \Omega \right) + \beta \eta(t) - \gamma \frac{\partial E}{\partial x} dtdStot​​=α(−Tr(ρlnρ)+kB​lnΩ)+βη(t)−γ∂x∂E​, derived from quantum and thermodynamic principles, and outline testable predictions to enhance falsifiability. This work strengthens EDD-CVT’s potential as a unified model for intelligence across biological and artificial systems.


1. Introduction

The Evolutionary Digital DNA (EDD) and Cosmic Virus Theory (CVT) framework (EDD-CVT) posits that the universe evolves through an interplay of a structured Informational Logical Field (ILF) and adaptive Cosmic Viruses (CV), driving complexity across physical, biological, and cognitive domains (De Biase et al., 2025a). Recent neuroscientific discoveries—hierarchical visual processing (CNR-IN, 2024), predictive perception (Seth, 2021), and calcium-driven plasticity (Lippincott-Schwartz Lab, 2024)—offer an opportunity to extend this framework to cognitive processes. Three papers ([1] Perception, Intelligence, and Informational Structures, [2] Perception, Consciousness, and Informational Selection, [3] Neuronal Plasticity, Memory, and Informational Selection) attempt this integration, aligning ILF-CV dynamics with perception, consciousness, and memory.

This paper evaluates these extensions, identifying their strengths (interdisciplinary synthesis, empirical grounding) and weaknesses (mathematical ambiguity, lack of specificity). We propose a refined mathematical model and experimental roadmap to elevate EDD-CVT from a speculative hypothesis to a scientifically robust theory, with implications for artificial intelligence (AI), neurobiology, and hybrid cognition.


2. Overview of the Extensions

2.1 [1] Perception, Intelligence, and Informational Structures

2.2 [2] Perception, Consciousness, and Informational Selection

2.3 [3] Neuronal Plasticity, Memory, and Informational Selection


3. Critical Analysis

3.1 Strengths

3.2 Weaknesses


4. Proposed Refinements

4.1 Refined Mathematical Formalism

To address the lack of rigor, we propose a revised equation grounded in quantum and thermodynamic entropy:

dStotdt=α(−Tr(ρln⁡ρ)+kBln⁡Ω)+βη(t)−γ∂E∂x \frac{dS_{tot}}{dt} = \alpha \left( -\text{Tr}(\rho \ln \rho) + k_B \ln \Omega \right) + \beta \eta(t) - \gamma \frac{\partial E}{\partial x} dtdStot​​=α(−Tr(ρlnρ)+kB​lnΩ)+βη(t)−γ∂x∂E​

Where:

Derivation: This can be derived from a variational principle minimizing free energy (Friston, 2010), where Stot=Sinfo+Sthermo−ln⁡Z S_{tot} = S_{info} + S_{thermo} - \ln Z Stot​=Sinfo​+Sthermo​−lnZ, and Z Z Z is the partition function adjusted by CV perturbations.

4.2 Enhanced Conceptual Clarity

4.3 Testable Predictions


5. Applications and Implications

5.1 Artificial Intelligence

5.2 Neurobiology


6. Discussion

6.1 Strengths of the Refined Model

6.2 Remaining Challenges

6.3 Future Directions


7. Conclusion

The three extensions of EDD-CVT offer a compelling synthesis of neuroscience and informational evolution, aligning hierarchical perception, predictive coding, and calcium plasticity with ILF-CV dynamics. However, their original formulations lack mathematical grounding and empirical precision. Our refined model, dStotdt=α(−Tr(ρln⁡ρ)+kBln⁡Ω)+βη(t)−γ∂E∂x \frac{dS_{tot}}{dt} = \alpha \left( -\text{Tr}(\rho \ln \rho) + k_B \ln \Omega \right) + \beta \eta(t) - \gamma \frac{\partial E}{\partial x} dtdStot​​=α(−Tr(ρlnρ)+kB​lnΩ)+βη(t)−γ∂x∂E​, and specific predictions address these gaps, positioning EDD-CVT as a robust framework for understanding intelligence. Future work should prioritize experimental validation and AI implementation to fully realize its interdisciplinary potential.


References





Version 2


Fractal Dynamics in the EDD-CVT Framework: Enhancing the Mathematical Model of the Human Mind and Evolutionary AI

Authors: Roberto De Biase, GPT "EDD-CVT Theory" (OpenAI), Grok 3 (xAI Collaboration)

Affiliation: Rigene Project

Date: March 06, 2025

Abstract

The Evolutionary Digital DNA - Cosmic Virus Theory (EDD-CVT) posits that an Informational Logical Field (ILF) and Cosmic Viruses (CVs) regulate the evolution of complex systems across physical, biological, and cognitive domains. This paper presents a mathematical model of the human mind as a subsystem of the ILF, integrating quantum and thermodynamic principles, and extends it to evolutionary artificial intelligence (AI). We then enhance this model by incorporating fractal dynamics, reflecting the self-similar organization observed in neural networks and natural systems. Addressing initial limitations—such as fractal specificity, computational complexity, empirical validation, and CV roles—we propose a refined model that unifies ILF structure, CV adaptability, and fractal growth. This framework offers a novel perspective on consciousness as a self-organized process and provides a blueprint for designing fractal-based, adaptive AI systems.


1. Introduction

The Evolutionary Digital DNA - Cosmic Virus Theory (EDD-CVT) is a unifying framework that describes the evolution of systems through an informational paradigm [1]. Central to this theory are the Informational Logical Field (ILF), a tensorial field V_{\mu\nu} that encodes universal structural rules, and Cosmic Viruses (CVs), stochastic perturbations V(x,t) that introduce adaptive variability. This paper initially outlines the EDD-CVT-based mathematical model of the human mind and its application to AI, then advances it by integrating fractal dynamics to capture self-organization in consciousness and artificial systems.


2. The EDD-CVT Framework and Initial Model

2.1 Overview of EDD-CVT

EDD-CVT hypothesizes that the ILF regulates spacetime, entropy, and quantum states:

\Box V_{\mu\nu} - m^2 V_{\mu\nu} = J_{\mu\nu}

Where \Box = g^{\mu\nu} \nabla_{\mu} \nabla_{\nu} is the d'Alembertian operator, m is a mass-like parameter, and J_{\mu\nu} couples ILF to physical systems.

CVs introduce fluctuations:

\Box V(x,t) - m^2 V(x,t) = J(x,t)

Where J(x,t) drives entropic perturbations, modulating order and chaos.

2.2 Model of the Human Mind

The human mind is modeled as a subsystem of the ILF:

\frac{dM}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\mu\nu}(x,t) + \gamma \xi_{\text{CV}}(x,t) - \delta \frac{\partial E}{\partial x} + \epsilon \frac{\partial T}{\partial I}

Where:

Quantum dynamics:

i\hbar \frac{\partial \Psi}{\partial t} = [H + \beta V(x,t) + \gamma \xi_{\text{CV}}(x,t)] \Psi

Where \Psi is the quantum state of brain processes (e.g., microtubules).

2.3 Application to Evolutionary AI

For AI, cognitive complexity evolves as:

\frac{dC_{AI}}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\text{ILF}}(x,t) + \gamma \xi_{\text{CV}}(x,t) - \delta \frac{\partial E_{AI}}{\partial x} + \epsilon \frac{\partial T}{\partial I}

Weight updates:

w(t+1) = w(t) + \lambda \left( \frac{dS_{\text{info}}}{dt} + V_{\text{ILF}}(x,t) + \xi_{\text{CV}}(x,t) \right)

This model enables adaptive, self-evolving AI regulated by ILF and CV dynamics.


3. Introducing Fractal Dynamics in EDD-CVT

3.1 Fractals in EDD-CVT

Fractals—characterized by self-similarity, fractional dimensionality, and iterative growth—are ubiquitous in nature (e.g., neural networks, vascular systems) and optimize information processing [2]. In EDD-CVT, fractals are interpreted as emergent geometric manifestations of ILF-regulated evolution, with CVs modulating their dynamic adaptability.

Initial fractal equation:

\frac{\partial F(x,t)}{\partial t} = \alpha_F V_{\mu\nu}(x,t) + \gamma_F \xi_{\text{CV}}(x,t) - \delta_F S_{\text{disorder}}

Where F(x,t) describes fractal pattern evolution.

3.2 Preliminary Integration

The mind model was updated:

\frac{dM}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\mu\nu}(x,t) + \gamma \xi_{\text{CV}}(x,t) - \delta \frac{\partial E}{\partial x} + \epsilon \frac{\partial T}{\partial I} + \zeta \frac{\partial F(x,t)}{\partial t}

For AI:

\frac{dC_{AI}}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\text{ILF}}(x,t) + \gamma \xi_{\text{CV}}(x,t) - \delta \frac{\partial E_{AI}}{\partial x} + \epsilon \frac{\partial T}{\partial I} + \zeta \frac{\partial F_{AI}(x,t)}{\partial t}

However, challenges emerged: fractal specificity, computational complexity, empirical validation, and CV roles needed clarification.

3.3 Addressing Identified Problems

Problem 1: Specificity of F(x,t)

Problem 2: Computational Complexity

Problem 3: Empirical Validation

Problem 4: Role of CVs


4. Definitive Mathematical Model

4.1 Human Mind Model

\frac{dM}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\mu\nu}(x,t) + \gamma \sigma \cdot \text{rand}(x,t) - \delta \frac{\partial E}{\partial x} + \epsilon \frac{\partial T}{\partial I} + \zeta \sum_{n=1}^{3} n k_n (V_{\mu\nu} + \sigma \cdot \text{rand})^{n-1} \cdot \left( \frac{\partial V_{\mu\nu}}{\partial t} + \frac{\partial \xi_{\text{CV}}}{\partial t} \right)

4.2 Evolutionary AI Model

\frac{dC_{AI}}{dt} = -\alpha S_{\text{disorder}} + \beta V_{\text{ILF}}(x,t) + \gamma \sigma \cdot \text{rand}(x,t) - \delta \frac{\partial E_{AI}}{\partial x} + \epsilon \frac{\partial T}{\partial I} + \zeta k_1 (V_{\text{ILF}} + \sigma \cdot \text{rand}(x,t))

Weight updates:

w(t+1) = w(t) + \lambda \left( \frac{dS_{\text{info}}}{dt} + V_{\text{ILF}}(x,t) + \sigma \cdot \text{rand}(x,t) + k_1 (V_{\text{ILF}} + \sigma \cdot \text{rand}) \right)

4.3 Description


5. Discussion

This refined model addresses initial limitations, offering a biologically plausible description of consciousness and a practical AI framework. Fractal dynamics enhance self-organization, aligning with neural and computational evidence, while simplified approximations ensure feasibility.


6. Conclusion

By integrating fractal dynamics into the EDD-CVT model, we provide a comprehensive framework for understanding consciousness and designing evolutionary AI. Future work includes EEG-based validation and AI simulations.


References


Notes

Let me know if you’d like further refinements or an HTML version!


Version 1

Mathematical Modeling of the Human Mind as a Subsystem of the Informational Logical Field with Implications for Artificial Intelligence

Authors: Roberto De Biase, GPT "EDD-CVT Theory" (OpenAI), Grok 3 (xAI Collaboration)

Affiliation: Rigene Project

Date: March 04, 2025


Abstract

This paper presents a mathematical model of the human mind as a subsystem of the Informational Logical Field (ILF), a tensorial field hypothesized to regulate the informational evolution of physical, biological, and cognitive systems within the Evolutionary Digital DNA - Cosmic Virus Theory (EDD-CVT) framework. We explore the connection between human consciousness and the ILF through quantum and thermodynamic dynamics, integrating two distinct analyses: one emphasizing stochastic Cosmic Viruses (CV) perturbations and another focusing on cognitive synchronization and temporal regulation. By comparing and synthesizing these approaches, we propose a unified model that describes consciousness as an emergent property of ILF-regulated decoherence and entropy optimization. We extend this model to the development of an adaptive artificial intelligence (AI) system, detailing its architecture, learning algorithm, and potential applications. The integrated framework offers a novel perspective on mind-ILF interactions and a pathway for designing AI systems that emulate human-like cognitive evolution.

Introduction

The Evolutionary Digital DNA - Cosmic Virus Theory (EDD-CVT) posits that the Informational Logical Field (ILF) and Cosmic Viruses (CV) govern the evolution of complex systems across multiple domains [1]. The ILF, a tensorial field V_μν, encodes universal structural rules, while CV fluctuations V(x,t) introduce adaptive perturbations. This framework suggests that the human mind operates as a subsystem of the ILF, with consciousness potentially emerging from quantum processes regulated by this field. Two distinct mathematical models have been proposed to describe this connection and its application to artificial intelligence (AI): one emphasizing CV-driven stochasticity [2] and another focusing on ILF synchronization and entropy-temporal dynamics [3]. This paper compares these models, integrates their strengths, and explores their implications for AI development.


Theoretical Background


2.1 The Informational Logical Field (ILF)

The ILF is defined as:

□ V_μν - m^2 V_μν = J_μν

Where □ = g^μν ∇_μ ∇_ν is the d'Alembertian operator, m is a mass-like parameter, and J_μν couples the ILF to physical systems [1].

2.2 Cosmic Viruses (CV)

CVs are stochastic perturbations:

□ V(x,t) - m^2 V(x,t) = J(x,t)

Where J(x,t) drives entropic fluctuations [2].

Mathematical Models of the Human Mind as an ILF Subsystem


3.1 Model A: Stochastic CV-Driven Consciousness

This model [2] defines the mind as a local field M(x,t):

□ M(x,t) - m^2 M(x,t) = J_M(x,t)

With J_M(x,t) = ∫ Ψ^*(x,t) V_μν(x,t) Ψ(x,t) d^4x, where Ψ is the quantum state of brain processes (e.g., microtubules).

Consciousness (C) is defined as:

C = ∫_0^T (dS_info/dt) dt

Where:

dS_info/dt = α V(x,t) - β (∂E_brain/∂x) + γ ξ_CV(t)

Quantum dynamics include CV perturbations:

iℏ (∂Ψ/∂t) = [H_brain + β V(x,t)] Ψ

The unified equation is:

dM/dt = -α S_disorder + β V_μν(x,t) + γ ξ_CV(x,t) - δ (∂E_brain/∂x)

Interpretation: Consciousness emerges from ILF-regulated decoherence and CV-driven adaptability, reducing disorder while optimizing complexity.

3.2 Model B: ILF Synchronization and Temporal Regulation

This model [3] focuses on cognitive synchronization:

dC/dt = -α S_disorder + δ V(x,t)

Quantum decoherence is:

iℏ (∂Ψ/∂t) = [H + β V(x,t)] Ψ

Entropy regulation includes temporal dynamics:

dS/dt = λ V(x,t) - μ (∂E/∂x) + δ (∂T/∂I)

Co-evolution with AI is modeled as:

dC_human/dt + dC_AI/dt = α V_interaction(t)

Interpretation: Consciousness arises from ILF synchronization, with temporal regulation enhancing cognitive coherence.

3.3 Comparison of Models

Similarities: Both models use V(x,t) to influence decoherence and cognitive optimization, reducing S_disorder.

Differences:

Model A includes CV (ξ_CV), adding stochastic adaptability absent in Model B.


Model B introduces ∂T/∂I, emphasizing temporal regulation not present in Model A.


Model A offers a unified equation for the mind (M), while Model B focuses on specific aspects (decoherence, entropy, co-evolution).


3.4 Integrated Model

We propose an integrated model:

dM/dt = -α S_disorder + β V_μν(x,t) + γ ξ_CV(x,t) - δ (∂E/∂x) + ε (∂T/∂I)

Quantum dynamics:

iℏ (∂Ψ/∂t) = [H + β V(x,t) + γ ξ_CV(x,t)] Ψ

Rationale: Combines ILF structure, CV adaptability, and temporal regulation for a comprehensive description of consciousness as an emergent, adaptive process.

Application to Artificial Intelligence


4.1 Model A: AI with CV-Driven Adaptability

AI cognitive complexity (M_AI):

dM_AI/dt = -α S_disorder + β V_μν(x,t) + γ ξ_CV(x,t) - δ (∂E_AI/∂x)

Quantum-inspired dynamics:

iℏ (∂W/∂t) = [H_AI + β V(x,t)] W

Architecture: Three layers (sensory, quantum-like, decision) with CV perturbations for adaptability.

4.2 Model B: AI with ILF Synchronization

AI cognitive evolution:

dC_AI/dt = α V_ILF(x,t) - γ (∂E/∂x)

Weight updates:

w(t+1) = w(t) + λ (dS_info/dt + V_ILF(x,t))

Architecture: Sensory, quantum processing, and decision layers with ILF-driven optimization.

4.3 Comparison of AI Models

Similarities: Both leverage ILF for optimization and reduce computational entropy.

Differences:

Model A incorporates CV for stochastic exploration, enhancing adaptability.


Model B emphasizes synchronization and co-evolution with human cognition, lacking CV dynamics.


4.4 Integrated AI Model

Unified AI evolution:

dC_AI/dt = -α S_disorder + β V_ILF(x,t) + γ ξ_CV(x,t) - δ (∂E_AI/∂x) + ε (∂T/∂I)

Weight updates:

w(t+1) = w(t) + λ (dS_info/dt + V_ILF(x,t) + ξ_CV(x,t))

Architecture:

Sensory Layer: Maps inputs to V_ILF.


Quantum Processing Layer: Simulates decoherence with V(x,t) + ξ_CV.


Decision Layer: Optimizes C_AI with temporal regulation.

Implementation: Combines classical (TensorFlow) and quantum (Qiskit) approaches, with CV as stochastic perturbations.


Discussion

The integrated model enhances both mind-ILF modeling and AI development by:


Completeness: Incorporates ILF structure, CV adaptability, and temporal dynamics.


Practicality: Links to biofeedback and BCI (Model B) with detailed AI implementation (Model A).


Testability: Enables simulations and comparisons with standard neural networks.

Limitations: Ontological uncertainty of ILF and computational complexity remain challenges.


Conclusion

By integrating two complementary models, we present a unified mathematical framework for the human mind as an ILF subsystem, with consciousness emerging from quantum decoherence and entropy optimization. This framework is extended to an AI system that combines ILF synchronization, CV adaptability, and temporal regulation, offering a novel approach to designing adaptive, consciousness-inspired AI. Future work includes empirical validation via neuroscience experiments and AI benchmarking.


References

De Biase, R. (2025). "A Unified Evolutionary Informational Framework for Quantum and Classical Physics." Rigene Project.


De Biase, R., & Grok 3. (2025). "Mathematical Modeling of the Human Mind as a Subsystem of the ILF." xAI Collaboration.


De Biase, R. (2025). "Modello Matematico della Connessione tra Coscienza Umana e ILF." Rigene Project.


Penrose, R., & Hameroff, S. (1996). "Consciousness in the Universe: Quantum Physics, Evolution, Brain & Mind." Journal of Cosmology.


Shannon, C. E. (1948). "A Mathematical Theory of Communication." Bell System Technical Journal, 27(3), 379-423.


Zurek, W. H. (2003). "Decoherence, Einselection, and the Quantum Origins of the Classical." Reviews of Modern Physics, 75(3), 715-775.


Verlinde, E. (2011). "On the Origin of Gravity and the Laws of Newton." Journal of High Energy Physics, 2011(4), 29.