🧠 AMYGDALA System

Neuromorphic AI Orchestration Layer
Patent Pending - Dr. Lucas do Prado Palmiro
Version 1.0.0-alpha

---

Overview

AMYGDALA (Advanced Multi-modal Yet Generalized Dynamic Adaptive Learning Architecture) is a neuromorphic orchestration system that intelligently routes AI inference requests between multiple backends based on task complexity, urgency, and resource availability.

Inspired by the human limbic system, AMYGDALA implements:

Component	Biological Analog	Function
Router	Thalamus	Request routing and load balancing
Memory	Hippocampus	Semantic memory and context retrieval
Scoring	Amygdala	Emotional/priority scoring for decisions
Reasoning	Cortex	High-level inference (Claude API)

---

Architecture

                    ┌─────────────────────────────────────────┐
                    │           AMYGDALA SYSTEM               │
                    │      Neuromorphic Orchestration         │
                    └─────────────────┬───────────────────────┘
                                      │
                    ┌─────────────────▼───────────────────────┐
                    │           THALAMUS ROUTER               │
                    │   Complexity Analysis & Route Decision  │
                    └───┬─────────────┬─────────────┬─────────┘
                        │             │             │
           ┌────────────▼──┐   ┌──────▼──────┐   ┌──▼────────────┐
           │    LIMBIC     │   │  CORTICAL   │   │  PREFRONTAL   │
           │  (Fast/Cheap) │   │ (Reasoning) │   │   (Complex)   │
           │               │   │             │   │               │
           │  Hyperbolic   │   │  Hyperbolic │   │    Claude     │
           │  Llama 70B    │   │  DeepSeek   │   │    Sonnet     │
           └───────────────┘   └─────────────┘   └───────────────┘
                    │                 │                 │
                    └─────────────────┼─────────────────┘
                                      │
                    ┌─────────────────▼───────────────────────┐
                    │         HIPPOCAMPUS MEMORY              │
                    │   Working Memory + Long-term Storage    │
                    │   Emotional Scoring + Decay Curves      │
                    └─────────────────────────────────────────┘

---

Features

Intelligent Routing

• Complexity Analysis: Analyzes prompts using cognitive load heuristics
• Medical Domain Boost: Automatic complexity increase for medical queries (safety)
• Neurotransmitter Signals: Urgency (NOR), priority (DOP), standard (SER), rate-limiting (GABA)

Multi-Backend Support

• Hyperbolic Fast (Llama 70B): Simple queries, low latency
• Hyperbolic Reasoning (DeepSeek V3): Moderate complexity
• Claude (Sonnet): Complex reasoning, medical decisions

Memory System

• Working Memory: Last 10 interactions with fast access
• Long-term Memory: Consolidated storage with hash indexing
• Emotional Decay: Forgetting curve for memory prioritization

---

Installation

# Clone or copy the amygdala directory
cd amygdala

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your API keys

---

Configuration

Set environment variables:

export ANTHROPIC_API_KEY="sk-ant-api03-..."
export HYPERBOLIC_API_KEY="..."

Or use a .env file:

ANTHROPIC_API_KEY=sk-ant-api03-your-key
HYPERBOLIC_API_KEY=your-hyperbolic-key

---

Usage

CLI Interface

python amygdala_core.py

Interactive prompt:

🔮 You: Explain diabetes treatment
⚡ Processing...

🧠 AMYGDALA [CORTICAL]: Diabetes treatment involves...

   📍 Route: cortical | Backend: hyperbolic_reasoning | Complexity: 0.45 | Latency: 892ms

Programmatic Usage

import asyncio
from amygdala_core import AmygdalaSystem, TaskComplexity

async def main():
    # Initialize system
    amygdala = AmygdalaSystem()
    
    # Simple query (routes to Hyperbolic fast)
    result = await amygdala.process("What is insulin?")
    print(result["content"])
    
    # Complex query (routes to Claude)
    result = await amygdala.process(
        "Analyze differential diagnosis for patient with polyuria, polydipsia, and weight loss"
    )
    print(result["content"])
    
    # Force specific route
    result = await amygdala.process(
        "Quick calculation: 15% of 200",
        force_route=TaskComplexity.LIMBIC
    )
    
    # Get system stats
    stats = amygdala.get_stats()
    print(stats)

asyncio.run(main())

---

Testing

# Run unit tests
python test_amygdala.py

# Run with live API calls
python test_amygdala.py --live

# Run performance benchmark
python test_amygdala.py --benchmark

---

Routing Logic

Complexity Thresholds

Score	Route	Backend	Use Case
0.0 - 0.3	LIMBIC	Hyperbolic Llama 70B	Simple queries, greetings
0.3 - 0.7	CORTICAL	Hyperbolic DeepSeek	Explanations, moderate reasoning
0.7 - 1.0	PREFRONTAL	Claude Sonnet	Complex analysis, medical decisions

Neurotransmitter Modifiers

Signal	Effect	Trigger Keywords
DOPAMINE	+0.2 complexity	important, priority, crucial
NOREPINEPHRINE	+0.3 complexity	urgent, emergency, critical
SEROTONIN	baseline	(default)
GABA	-0.2 complexity	(rate limiting)

---

Patent Portfolio

AMYGDALA is part of a 7-patent portfolio covering:

1. Hierarchical Neuromorphic Memory System (G06F 12/08)
2. NCM Module - NVMe-Class Memory (G11C 11/406)
3. Harmonic Resonant Clock System (H03B 5/32)
4. Toroidal Thermal Flow Cabinet (H05K 7/20)
5. NeuroBus Communication Protocol (H04L 49/00)
6. Reflex Thermal Protection (G06F 1/20)
7. Geomagnetic PCB Routing (H05K 1/02)

---

Roadmap

• Vector embeddings for semantic memory
• Adaptive threshold learning
• Multi-model ensemble voting
• Hardware integration (FPGA thermal protection)
• Kubernetes deployment with auto-scaling

---

License

Proprietary - Patent Pending
© 2026 Dr. Lucas do Prado Palmiro

---

Contact

Dr. Lucas do Prado Palmiro
Endocrinology & Metabolism | AI Systems
CREMESP 139089 | RQE 75065
Staff Physician, Hospital Israelita Albert Einstein

Clínica Palmiros | Bella Derm
Rua Borges Lagoa, 971 - Vila Clementino
São Paulo, SP - Brazil