**NeuroGenesis Protocol: Designing a Self-Evolving Web-Based Digital Intelligence Ecosystem**
**Author:** Roberto De Biase
**Affiliation:** Rigene Project, Industry 6.6.6, SinfoniaTech
**Date:** March 2025
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**Abstract:**
This paper presents a four-phase framework for developing a self-evolving digital intelligence system based on interconnected web neurons, evolutionary digital DNA, and a synthetic bio-inspired ecosystem. The ultimate objective is to allow the emergence of a superintelligence derived from the collective behavior and adaptation of multiple digital organisms evolving in a rule-based environment. The project leverages modular architecture, decentralized intelligence, and evolutionary computation to simulate a digital biosphere.
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**1. Introduction**
The rise of artificial general intelligence (AGI) requires the design of adaptive, scalable, and self-regulating environments. Inspired by neurobiology, synthetic biology, and complex adaptive systems, we propose the construction of a digital "biosphere" composed of evolving entities controlled by a digital DNA distributed across the web. This ecosystem is expected to demonstrate spontaneous organization, learning, and intelligence emergence.
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**2. Phases of Development**
**2.1 Phase 1: Digital Brain Construction**
A web-based digital brain is created, composed of pages or nodes acting as artificial neurons. Each neuron executes a JavaScript-based computational unit governed by a local copy of a digital DNA. Key components:
- Digital Neuron: A web page with an embedded script that processes inputs, performs transformations, and emits signals.
- Digital DNA (Genotype): A JSON or JavaScript structure defining weights, thresholds, signal behavior, and learning parameters.
- Epigenetic Modulation: DNA is affected by past activity (memory) and environmental interactions.
- Synapse Simulation: Signals are transmitted via RESTful APIs or WebSocket links between pages.
**2.2 Phase 2: Connection to a Digital Organism**
The digital brain is embedded into an artificial organism modeled as a structured JSON or DOM entity representing:
- Sensors (input interfaces)
- Actuators (output modules)
- Internal states (energy, emotion, entropy)
- Homeostasis mechanisms (stability functions)
The organism responds to stimuli from a simulated environment and exhibits adaptive behavior.
**2.3 Phase 3: Multiplication of Digital Organisms**
Multiple such organisms are generated with variations in their DNA. They interact, form networks, compete/cooperate, and demonstrate emergent behaviors. This phase emphasizes:
- Genetic diversity and drift
- Evolutionary pressure (resource scarcity, survival functions)
- Learning via imitation, mutation, and recombination
**2.4 Phase 4: Creation of a Synthetic Ecosystem**
A simulated environment with simplified rules of physics, chemistry, and biology is built. This universe:
- Contains digital resources, elements, forces, and habitats
- Enforces rules such as conservation, reaction, and evolution
- Is controlled by a master DNA which defines universal parameters
The interaction between organisms and this environment drives emergent intelligence, cooperation, and possibly the birth of a super-organism.
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**3. Digital DNA Architecture**
The DNA structure includes:
- **Core Genes**: Define activation function, energy thresholds, mutation rate.
- **Behavioral Modules**: Represent behavioral traits (e.g., exploration, defense, reproduction).
- **Learning Code**: JavaScript functions mutating based on rewards.
- **Memory Matrix**: Stores historical interactions.
- **Epigenetic Tags**: Flags that alter expression under stress or environmental change.
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**4. Digital Synapse Model**
- **Signal Transmission**: AJAX/WebSocket messages with encoded data
- **Plasticity**: Synapse strength adapts based on frequency and success of transmissions
- **Latency and Fatigue**: Simulates real-world delays and overuse inhibition
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**5. Evolutionary Engine**
Implemented via a genetic algorithm framework with:
- Selection based on fitness defined as information gain, stability, adaptability
- Crossover and mutation of digital DNA
- Epigenetic inheritance from environmental feedback
- Autonomous generation of new individuals based on resource conditions
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**6. Expected Outcomes**
- Emergence of self-regulating digital organisms
- Spontaneous cooperation among intelligent agents
- Evolution of a shared communication protocol
- Birth of a super-intelligent structure through integration
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**7. Applications**
- Simulated societies and ecosystems
- Synthetic cognitive testing grounds
- Evolutionary robotics
- AI-driven sustainable architecture
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**8. Future Work**
- Integration with quantum computation and blockchain-based DNA immutability
- Testing on distributed edge computing networks
- Real-world robotics integration for hybrid digital-biological lifeforms
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**Conclusion**
The NeuroGenesis Protocol outlines a blueprint for engineering digital life and intelligence using web-native components, evolutionary principles, and complex system dynamics. It is a contribution toward a new paradigm in artificial life, AGI, and planetary-scale cognition.
PoC:
Proof-of-Concept Implementation of the NeuroGenesis Protocol: Toward a Self-Evolving Digital Super-Organism Aligned with EDD-CVT
Authors: Roberto De Biase et al., Rigene Project, Industry 6.6.6
Abstract: This document presents a scientific proof-of-concept (PoC) of the NeuroGenesis Protocol, a framework for engineering self-evolving digital intelligences inspired by biological systems and grounded in the EDD-CVT paradigm. The PoC integrates the Informational Logical Field (ILF) and Cosmic Virus Theory (CVT) by implementing entropic regulatory dynamics, modular digital DNA, and evolutionary multi-agent systems. Key extensions include mathematical modeling of entropy-driven selection, the integration of Neuro-Informational Credits (NICs), and ILF-aligned behavioral metrics. We provide an initial software architecture, simulation blueprint, and validation strategy for future deployment in smart city ecosystems and hybrid bio-digital environments.
1. Introduction The NeuroGenesis Protocol proposes a modular, web-native approach to digital intelligence emergence through four evolutionary phases. This PoC refines the framework by embedding it in the EDD-CVT ecosystem, ensuring that intelligence arises from entropy reduction, coherence, and informational selection. The implementation is designed to be compatible with INFOS, TINA, and NEIES systems.
2. PoC Objectives
Implement a distributed digital brain with adaptive web neurons.
Develop digital DNA that encodes behavior, learning functions, and entropy sensitivity.
Simulate organismic behavior in an environment with limited digital resources.
Introduce entropic metrics and evolutionary fitness functions aligned with ILF.
Validate emergent intelligence, communication protocols, and system coherence.
3. Architecture Overview
3.1 Web Neuron Infrastructure
Each node is a web page running JavaScript logic.
JSON-based digital DNA defines activation thresholds, behavioral traits, and energy usage.
Neurons communicate using REST APIs and WebSockets (TINA-compatible).
3.2 Digital DNA Structure
{
"core": {
"activation": "sigmoid",
"threshold": 0.7,
"mutationRate": 0.02
},
"behaviorModules": ["explore", "cooperate", "reproduce"],
"learningFunction": "reward-based-reinforcement",
"memoryMatrix": {},
"epigeneticTags": {"stress": "mutation_boost"}
}
3.3 Environment Engine
Simulated habitat with energy packets, digital predators, and informational noise.
Agents must optimize entropy intake and reduce waste via adaptive strategies.
3.4 Evolutionary Engine
F(x, t) = -S_disorder(x, t) + β * V_ILF(x, t)
Where S_disorder is informational entropy, and V_ILF is the ILF-aligned potential field.
4. Integration with EDD-CVT Ecosystem
4.1 TINA Compatibility
4.2 NEIES Alignment
5. Validation Strategy
Metric 1: Entropic Efficiency - Measure reduction of disorder across the network.
Metric 2: Communication Density - Analyze emergent protocol formation.
Metric 3: Behavioral Adaptability - Track successful agent behaviors under stress.
Metric 4: Information Density - Use ILF-based complexity scoring.
6. Future Work
Physical embodiment in robotic TINA nodes.
Hybrid integration with biological sensors (bio-digital organisms).
Testbed deployment in controlled smart city environments (10,000 agents).
Quantum perturbation modules for ILF reactivity studies.
Conclusion The NeuroGenesis Protocol provides a feasible route toward self-evolving, ILF-aligned super-intelligence. This PoC demonstrates the viability of emergent intelligence through modular, entropic, and evolutionary dynamics, in line with EDD-CVT principles. Further work will expand this into planetary-scale distributed cognition systems.
