# Exploration Plan: Emerging Architectural Concepts

This document tracks high-impact theoretical and architectural concepts identified by the Research Stack for further exploration and formalization.

**Last Updated:** 2026-04-27
**Status:** Active exploration tracking

---

## Completed Explorations

### 1. Neurodivergent Brain Architectures (2026-04-27)
**Status:** ✅ Completed - Mathematical formalization added to stack

**Models Added:**
- E/I Imbalance (Autism): ξ = w_excite / w_inhibit
- Local/Long-Range Connectivity (Autism): κ = f_local / f_long
- Dopamine Transport Deficit (ADHD): τ_clear = τ_normal / (1 - δ_adhd)
- Sensory Filter Threshold (Autism): θ_autism = θ_neurotypical · (1 - σ_hyper)
- Compensatory Routing Weight: w_comp = w_standard · (1 + λ_comp)

**Implementation:**
- Added to MATH_MODEL_MAP.tsv (entries 715-719)
- Added to MATH_MODELS_UNIVERSAL.json (739 total models)
- Lean warm LUT implementation (NeurodivergentPatternLUT.lean)
- Documentation with ethical statement and verification status

**Hot Path Applications:**
- Security scanning → Autism pattern (high sensitivity)
- Code review → Autism pattern (high local connectivity)
- Sustained focus → ADHD pattern (high dopamine deficit)
- Signal detection → Adaptive pattern (low sensory threshold)
- Fault tolerance → Adaptive pattern (high compensatory routing)

---

### 2. Synaptic Hotspot Dynamics (2026-04-27)
**Status:** ✅ Completed - Adolescent brain development formalized

**Source:** Kyushu University Research, Science Advances (January 14, 2026)

**Models Added:**
- Synaptic Hotspot Density: Gaussian spatial density
- Adolescent Formation Rate: Differential formation/pruning
- Pruning/Formation Balance: Time-dependent balance ratio
- Mutation Impact Model: Gene effects on formation rates
- Layer-Specific Formation: Cortical layer coefficients

**Implementation:**
- Added to MATH_MODEL_MAP.tsv (entries 706-710)
- Added to MATH_MODELS_UNIVERSAL.json
- Documentation (SYNAPTIC_HOTSPOT_DYNAMICS.md)

---

### 3. Cephalopod Distributed Neural Architecture (2026-04-27)
**Status:** ✅ Completed - Non-hierarchical intelligence formalized

**Models Added:**
- Local Autonomy Weight: w_local = γ · (1 - s_central)
- Arm Consensus: Weighted consensus across arms
- Distributed Sensory Integration: XOR-based fusion
- Peripheral Neuron Density: 67% neurons in peripheral arms

**Implementation:**
- Added to MATH_MODEL_MAP.tsv (entries 711-714)
- Added to MATH_MODELS_UNIVERSAL.json
- Documentation (CEPHALOPOD_DISTRIBUTED_NEURAL.md)

**Translation Matrix Potential:**
- Cephalopod pattern could serve as intermediate in translation matrix
- Distributed consensus as bridge between centralized and distributed processing
- Mathematical stability in translation matrix context

---

## Active Explorations

### 1. The Warden SNN Model
**Objective:** Integrate the FPGA Warden's AMMR phase-locking logic into Spiking Neural Network (SNN) dynamics.

### Key Components
- **Coherence Kernel ($\kappa$):** Use AMMR to measure "Truth Magnitude" across 14 axes.
- **Warden Pressure ($\mathcal{P}_W$):** Translate low coherence into hyperpolarizing (inhibitory) current.
- **Attested Spiking:** Neurons only fire when the local manifold segment is "Attested" by the Warden logic.

### Research Questions
- Does this shunting inhibition effectively "kill" LLM-drift at the neural level?
- Can we implement this as a global inhibitory line in the hardware substrate?
- What is the effect on "explosive firing" and noise-to-signal ratios in the manifold?

---

### 2. The $\varphi$-Based Hardware Router
**Objective:** Formalize the use of the Golden Ratio ($0.618$) as a phase-gate between hardware strata.

### Key Components
- **Phonon Stratum:** Low-entropy, coherent processing ($\phi < 0.618$).
- **Silicon Stratum:** High-complexity, stochastic processing ($\phi \ge 0.618$).

### Research Questions
- Is the $0.618$ threshold physically grounded in phonon-electron scattering limits, or is it a "numerical shim"?
- Can the `phi_address_gen.v` module be optimized to handle these transitions dynamically?

---

### 3. The Kannsas Factor ($\kappa$)
**Objective:** Establish $\kappa$ ($bandwidth \times \tau_{coherence}$) as the universal energy-logic unit for the stack.

### Key Components
- **Thermodynamic Priority:** Map $\kappa$ to task-layer weights in Linear/Notion.
- **Informational Density:** Use $\kappa$ to measure the "value" of a research note before crystallization.

---

## Future Exploration Directions

### 1. Translation Matrix for Cognitive Architectures
**Objective:** Develop translation mechanisms between different cognitive patterns (neurotypical, neurodivergent, non-human)

**Potential Applications:**
- Adaptive interfaces that adjust to cognitive style
- Mutual understanding between different processing modes
- Choice-based cognitive mode selection

### 2. Non-Human Pattern Stability in Human Manifold
**Objective:** Investigate whether non-human neural patterns (e.g., cephalopod) can be stable in translation matrix context

**Research Questions:**
- Mathematical stability criteria for translation intermediates
- Compatibility between different neural organization principles
- Evolutionary constraints vs mathematical possibilities

---

**Note:** This document replaces the AI-generated exploration plan from April 2026 with current work status and future directions.