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136 lines
5.3 KiB
Markdown
136 lines
5.3 KiB
Markdown
# Exploration Plan: Emerging Architectural Concepts
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This document tracks high-impact theoretical and architectural concepts identified by the Research Stack for further exploration and formalization.
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**Last Updated:** 2026-04-27
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**Status:** Active exploration tracking
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---
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## Completed Explorations
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### 1. Neurodivergent Brain Architectures (2026-04-27)
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**Status:** ✅ Completed - Mathematical formalization added to stack
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**Models Added:**
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- E/I Imbalance (Autism): ξ = w_excite / w_inhibit
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- Local/Long-Range Connectivity (Autism): κ = f_local / f_long
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- Dopamine Transport Deficit (ADHD): τ_clear = τ_normal / (1 - δ_adhd)
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- Sensory Filter Threshold (Autism): θ_autism = θ_neurotypical · (1 - σ_hyper)
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- Compensatory Routing Weight: w_comp = w_standard · (1 + λ_comp)
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**Implementation:**
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- Added to MATH_MODEL_MAP.tsv (entries 715-719)
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- Added to MATH_MODELS_UNIVERSAL.json (739 total models)
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- Lean warm LUT implementation (NeurodivergentPatternLUT.lean)
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- Documentation with ethical statement and verification status
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**Hot Path Applications:**
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- Security scanning → Autism pattern (high sensitivity)
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- Code review → Autism pattern (high local connectivity)
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- Sustained focus → ADHD pattern (high dopamine deficit)
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- Signal detection → Adaptive pattern (low sensory threshold)
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- Fault tolerance → Adaptive pattern (high compensatory routing)
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---
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### 2. Synaptic Hotspot Dynamics (2026-04-27)
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**Status:** ✅ Completed - Adolescent brain development formalized
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**Source:** Kyushu University Research, Science Advances (January 14, 2026)
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**Models Added:**
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- Synaptic Hotspot Density: Gaussian spatial density
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- Adolescent Formation Rate: Differential formation/pruning
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- Pruning/Formation Balance: Time-dependent balance ratio
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- Mutation Impact Model: Gene effects on formation rates
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- Layer-Specific Formation: Cortical layer coefficients
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**Implementation:**
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- Added to MATH_MODEL_MAP.tsv (entries 706-710)
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- Added to MATH_MODELS_UNIVERSAL.json
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- Documentation (SYNAPTIC_HOTSPOT_DYNAMICS.md)
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---
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### 3. Cephalopod Distributed Neural Architecture (2026-04-27)
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**Status:** ✅ Completed - Non-hierarchical intelligence formalized
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**Models Added:**
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- Local Autonomy Weight: w_local = γ · (1 - s_central)
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- Arm Consensus: Weighted consensus across arms
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- Distributed Sensory Integration: XOR-based fusion
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- Peripheral Neuron Density: 67% neurons in peripheral arms
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**Implementation:**
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- Added to MATH_MODEL_MAP.tsv (entries 711-714)
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- Added to MATH_MODELS_UNIVERSAL.json
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- Documentation (CEPHALOPOD_DISTRIBUTED_NEURAL.md)
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**Translation Matrix Potential:**
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- Cephalopod pattern could serve as intermediate in translation matrix
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- Distributed consensus as bridge between centralized and distributed processing
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- Mathematical stability in translation matrix context
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---
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## Active Explorations
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### 1. The Warden SNN Model
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**Objective:** Integrate the FPGA Warden's AMMR phase-locking logic into Spiking Neural Network (SNN) dynamics.
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### Key Components
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- **Coherence Kernel ($\kappa$):** Use AMMR to measure "Truth Magnitude" across 14 axes.
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- **Warden Pressure ($\mathcal{P}_W$):** Translate low coherence into hyperpolarizing (inhibitory) current.
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- **Attested Spiking:** Neurons only fire when the local manifold segment is "Attested" by the Warden logic.
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### Research Questions
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- Does this shunting inhibition effectively "kill" LLM-drift at the neural level?
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- Can we implement this as a global inhibitory line in the hardware substrate?
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- What is the effect on "explosive firing" and noise-to-signal ratios in the manifold?
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---
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### 2. The $\varphi$-Based Hardware Router
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**Objective:** Formalize the use of the Golden Ratio ($0.618$) as a phase-gate between hardware strata.
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### Key Components
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- **Phonon Stratum:** Low-entropy, coherent processing ($\phi < 0.618$).
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- **Silicon Stratum:** High-complexity, stochastic processing ($\phi \ge 0.618$).
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### Research Questions
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- Is the $0.618$ threshold physically grounded in phonon-electron scattering limits, or is it a "numerical shim"?
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- Can the `phi_address_gen.v` module be optimized to handle these transitions dynamically?
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---
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### 3. The Kannsas Factor ($\kappa$)
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**Objective:** Establish $\kappa$ ($bandwidth \times \tau_{coherence}$) as the universal energy-logic unit for the stack.
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### Key Components
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- **Thermodynamic Priority:** Map $\kappa$ to task-layer weights in Linear/Notion.
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- **Informational Density:** Use $\kappa$ to measure the "value" of a research note before crystallization.
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---
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## Future Exploration Directions
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### 1. Translation Matrix for Cognitive Architectures
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**Objective:** Develop translation mechanisms between different cognitive patterns (neurotypical, neurodivergent, non-human)
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**Potential Applications:**
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- Adaptive interfaces that adjust to cognitive style
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- Mutual understanding between different processing modes
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- Choice-based cognitive mode selection
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### 2. Non-Human Pattern Stability in Human Manifold
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**Objective:** Investigate whether non-human neural patterns (e.g., cephalopod) can be stable in translation matrix context
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**Research Questions:**
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- Mathematical stability criteria for translation intermediates
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- Compatibility between different neural organization principles
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- Evolutionary constraints vs mathematical possibilities
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---
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**Note:** This document replaces the AI-generated exploration plan from April 2026 with current work status and future directions.
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