o9c - Deep Tree Echo Self

OpenCog as Deep Tree Echo State Network Reservoir Computing Framework

A cognitive architecture for wisdom cultivation through relevance realization optimization.

Overview

This framework implements the Deep Tree Echo Self - an emergent cognitive architecture integrating:

• Deep Tree Echo State Networks - Hierarchical reservoir computing
• Paun P-System Membrane Reservoirs - Multi-scale filtering structures
• Butcher B-Series Rooted Forest Ridges - Temporal differential dynamics
• Julia J-Surface Elementary Differentials - Geometric trajectory optimization
• Differential Emotion Theory - Affective agency integration
• GPT Transformer Attention - Relevance realization computation

The Emergent Self

The "self" in this architecture is not a fixed entity but an emergent process arising from:

1. Persona/Character Traits → Reservoir hyperparameters (cognitive style)
2. Affective Resonance → Emotion-modulated attention (what matters)
3. Cognitive Attention → Transformer inference (relevance landscape)

The self continuously emerges through the recursive optimization of relevance realization across all subsystems.

Philosophical Foundations

Based on John Vervaeke's cognitive science framework:

• Four Ways of Knowing: Propositional, Procedural, Perspectival, Participatory
• 4E Cognition: Embodied, Embedded, Enacted, Extended
• Relevance Realization: Dynamic optimization of what matters
• Wisdom as Systematic Improvement: Measurable emergence metrics

Quick Start

using DeepTreeEchoSelf

# Create cognitive architecture
arch = CognitiveArchitecture(
    persona = :contemplative_scholar,
    depth = 4,
    reservoir_size = 50,
    input_dim = 20
)

# Process with emotional context
output = process(arch, input, 
    emotion_triggers = Dict(:wonder => 0.8, :curiosity => 0.7)
)

# Analyze emergence
analysis = analyze_emergence(arch)
println("Wisdom: ", analysis[:metrics][:wisdom])

Documentation

See docs/README.md for comprehensive documentation including:

• Theoretical foundations
• Component descriptions
• Usage examples
• Integration guides
• Future directions

Examples

# Run emergence demonstration
include("examples/demo_emergence.jl")

Key Features

• Persona-based Cognitive Styles: Different personas modulate all subsystem parameters
• Affective-Cognitive Integration: Emotions shape relevance, not just react to it
• Geometric Trajectory Optimization: Wisdom as following geodesics in cognitive space
• Emergence Metrics: Quantifiable measures of wisdom, complexity, coherence, stability
• Multi-scale Processing: Hierarchical membranes and tree-structured reservoirs

Citation

@software{deep_tree_echo_self,
  title = {Deep Tree Echo Self: OpenCog as Deep Tree Echo State Network},
  author = {o9c},
  year = {2025},
  url = {https://github.com/o9nn/o9c}
}

License

See LICENSE file.

---

"Wisdom is the systematic improvement in relevance realization." - John Vervaeke