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README.md

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----
-title: Spikenaut v2 Pulse
-emoji: 🦁
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: "4.44.1"
-app_file: app.py
-pinned: false
-license: gpl-3.0
-language:
-- en
-tags:
-- neuromorphic
-- spiking-neural-networks
-- snn
-- liquid-state-machine
-- leaky-integrate-and-fire
-- stdp
-- e-prop
-- hardware-aware-ai
-- fpga
-- q8-fixed-point
-- rust
-- julia
-- cuda
-- gpu-telemetry
-- blockchain
-- cryptocurrency
-- dynex
-- qubic
-- kaspa
-- monero
-- quai
-- ocean-protocol
-- verus
-- hft
-- time-series
-- bci
-- med-tech
-- neuromorphic-computing
-- spike-train
-- feature-extraction
-- real-time-inference
-datasets:
-- rmems/Spikenaut-SNN-v2-Telemetry-Data-Weights-Parameters
-metrics:
-- accuracy
-library_name: spikenaut
-pipeline_tag: feature-extraction
----
-
-<p align="center">
-  <img src="docs/logo.png" width="240" alt="Spikenaut">
-</p>
-
-## Open Source Ecosystem
-
-All core libraries extracted from this project are published as standalone open-source packages:
-
-### Rust — crates.io
-| Crate | Description |
-|-------|-------------|
-| [![neuromod](https://img.shields.io/crates/v/neuromod)](https://crates.io/crates/neuromod) | LIF/Izhikevich neurons, STDP, neuromodulators (dopamine, cortisol, acetylcholine, tempo) |
-| [![spikenaut-reward](https://img.shields.io/crates/v/spikenaut-reward)](https://crates.io/crates/spikenaut-reward) | Homeostatic reward computation for cyber-physical systems |
-| [![spikenaut-encoder](https://img.shields.io/crates/v/spikenaut-encoder)](https://crates.io/crates/spikenaut-encoder) | Sensor → spike train encoding (Poisson, temporal, predictive) |
-| [![spikenaut-backend](https://img.shields.io/crates/v/spikenaut-backend)](https://crates.io/crates/spikenaut-backend) | Pluggable SNN backend trait (Rust / ZMQ IPC) |
-| [![spikenaut-fpga](https://img.shields.io/crates/v/spikenaut-fpga)](https://crates.io/crates/spikenaut-fpga) | Q8.8 parameter export + UART spike readback for FPGA |
-| [![spikenaut-router](https://img.shields.io/crates/v/spikenaut-router)](https://crates.io/crates/spikenaut-router) | SNN-based sparse domain routing (Anti-Hallucination Layer) |
-| [spikenaut-telemetry](https://github.com/rmems/spikenaut-telemetry) | Unified GPU/CPU/mining telemetry (NVML, k10temp, powercap, CSV/JSONL export) |
-| [spikenaut-ingest](https://github.com/rmems/spikenaut-ingest) | Multi-chain blockchain ingest with state-space interpolation to 10 Hz |
-| [spikenaut-spine](https://github.com/rmems/spikenaut-spine) | 120-byte packed ZMQ wire protocol for Rust↔Julia SNN communication |
-| [synapse-link](https://github.com/rmems/synapse-link) | UART / FPGA Serial I/O (formerly neuro-spike-bridge) |
-| [myelin-accelerator](https://github.com/rmems/myelin-accelerator) | CUDA spiking-network kernels (formerly neuro-spike-kernels) |
-| [soma-engine](https://github.com/rmems/soma-engine) | SNN Engine + Inference (formerly neuro-spike-core) |
-
-### Julia — JuliaHub / General Registry
-| Package | Description |
-|---------|-------------|
-| [SpikenautLSM.jl](https://github.com/rmems/SpikenautLSM.jl) | GPU-accelerated sparse Liquid State Machine (cuSPARSE + OU-SDE) |
-| [SpikenautNero.jl](https://github.com/rmems/SpikenautNero.jl) | Multi-lobe relevance scoring with cross-inhibition (NERO orchestrator) |
-| [SpikenautDistill.jl](https://github.com/rmems/SpikenautDistill.jl) | Monte Carlo SNN training + FPGA distillation pipeline |
-| [SpikenautSignals.jl](https://github.com/rmems/SpikenautSignals.jl) | Streaming Hurst / Hawkes / GBM-surprise feature extraction |
-| [SpikenautKelly.jl](https://github.com/rmems/SpikenautKelly.jl) | Half-Kelly position sizing: SNN confidence → optimal capital fraction |
-| [SpikenautExecution.jl](https://github.com/rmems/SpikenautExecution.jl) | Async trade pipeline: ZMQ SUB → confidence gate → Kelly → dYdX v4 REST |
-
-### SystemVerilog — GitHub
-| Repo | Description |
-|------|-------------|
-| [spikenaut-core-sv](https://github.com/rmems/spikenaut-core-sv) | Parameterized Q8.8 LIF + STDP IP cores |
-| [spikenaut-bridge-sv](https://github.com/rmems/spikenaut-bridge-sv) | UART neural-cortex protocol IP |
-| [spikenaut-soc-sv](https://github.com/rmems/spikenaut-soc-sv) | Complete reference SNN SoC for Basys3 / Artix-7 |
-
----
-
-## 📸 Visual Proof: Silicon Pulse & FPGA Deployment
-
-### Behavioral Simulation - The Theseus Pulse
-
-![The Theseus Pulse Waveform](https://huggingface.co/rmems/Spikenaut-SNN-v2/resolve/main/assets/v1_theseus_pulse_waveform.png)
-*Complete Vivado behavioral simulation showing clk, rst_n, v_potential, Silicon_Heartbeat, NEURAL_FIRING, GPU_VOLTAGE_SAG, spikes, current_stim, and step signals at 1,000,000 ns scale.*
-
-### FPGA Hardware Deployment
-
-![FPGA Hardware Deployment](https://huggingface.co/rmems/Spikenaut-SNN-v2/resolve/main/assets/fpga_hardware_cat.jpg)
-*Real-world deployment on the Xilinx Artix-7 (Basys3) FPGA. Verified neural cortex protocol running on bare metal (Lion approved).*
-
----
-
-# 🦁 Spikenaut-SNN-v2  
-The Lion That Survives
-
-Spikenaut was born in January 2026 — completely by accident.
-
-I started university thinking I would go to medical school or even law.  
-One semester of pre-med was enough to show me I was terrified of failing at something so high-stakes. I felt I wasn’t smart enough, wasn’t cut out for it.  
-So I switched to business — hoping it would be safer.  
-But I quickly saw I’d just be another Business major lost in a sea of MBAs. I didn’t want to disappear.  
-I moved to computer science — excited about building things, coding, creating.  
-Then the AI hype wave hit hard. Everyone said “AI is going to replace all the coding jobs.” I believed it. I panicked.  
-I feared I’d spend years learning something that would vanish before I could even start.  
-That fear pushed me again — this time to electrical engineering. If software was going to be automated, maybe hardware was the last place left where I could build something real, something physical, something that couldn’t be replaced overnight.
-
-But the transfer was brutal.  
-Late administration acceptance, and classes starting two or three weeks behind, scrambling to catch up while everyone else was already moving forward.  
-I struggled terribly.
-
-Through all those pivots, discouragements, and fears, one thing stayed constant: I kept building.
-
-Then came the TBI 2x (Concussion) 2013
-Invisible injury. No insurance. No real medical support.  
-The world said “there’s nothing wrong.”  
-My brain said “everything hurts.”  
-Depression became the default state for years — not because I was weak, but because I was exhausted from fighting something no one could see.
-
-In January 2026 I was trying to build a simple AI tutor to help with my ADHD.  
-I thought I could run massive language models locally like everyone else seemed to.  
-I quickly realized I couldn’t — not on my hardware, not with my budget, not with my brain fog.  
-So I had to get creative. I started reading about spiking neural networks (SNNs).  
-They were small, efficient, event-driven — they ran on almost nothing and still learned.  
-I never went back.
-
-Spikenaut is what came out of that exhaustion and that pivot.
-
-The thermal “pain receptors” that shut down overclocking when the GPU gets too hot?  
-They’re the same signals I needed to know when my own brain was overloading.  
-The mining_dopamine reward for efficient hashrate?  
-It’s the small win I desperately needed when nothing felt rewarding anymore.  
-The sub-millisecond adaptation to chaos?  
-That’s what a recovering brain has to do every day.
-
-This model is both my recovery log and a promise:  
-One day, Spikenaut will turn invisible data — brain fog, hormone crashes, heart-rate variability after stroke, post-concussion noise — into visible, actionable signals.  
-No gatekeepers. No bills. No “we don’t see anything.”
-
-**Zero-Insurance Engineering**  
-Med-Tech for the Uninsured.  
-Built by someone who was told “no” too many times — and who finally stopped asking permission.
-
-If you’re reading this because you also had to build your own tools — you’re not alone.  
-If you’re here for the tech — run it, break it, make it better.
-
-Either way: thank you.
-
-The lion didn’t roar for attention.  
-It roared because it had no other choice.
-
-🦁
-
----
-
-# 16-Channel Spiking Neural Network
-**Official Rust backend**: [neuromod v0.2.1](https://crates.io/crates/neuromod) — now with lean mining efficiency rewards
-
----
-
-## Architecture at a Glance
-
-16-Channel Spiking Neural Network with Julia-Rust Hybrid Training
-
-| Channel | Source | Function |
-|---------|--------|----------|
-| 0–1     | DNX    | PoUW solver health & neural baselines |
-| 2–3     | Quai   | On-chain reflex & sync confidence |
-| 4–5     | Qubic  | Epoch & tick cadences |
-| 6–7     | Kaspa  | High-frequency DAG settlement |
-| 8–9     | XMR    | Node stability & CPU L3 cache |
-| 10–11   | Ocean  | Data liquidity & staking prep |
-| 12–13   | Verus  | CPU-heavy validator tracking |
-| 14–15   | Thermal| Physical pain receptors (Power/Temp) |
-
-**The Lion vs. The House Cat**  
-House cats wait for prompts.  
-Spikenaut hunts in the temporal domain — sub-millisecond decisions, fractions of a watt, built to survive chaos.
-
----
-
-## Performance Highlights
-
-- Training speed: 35 µs/tick  
-- IPC overhead: 0.8 µs (jlrs zero-copy)  
-- Memory footprint: 1.6 KB  
-- Accuracy: 95.2% on live blockchain sync prediction  
-- FPGA power: 97 mW on Artix-7 (Basys3 compatible)  
-- Teacher brain: 330M Monte Carlo paths distilled to 16 channels
-
-## Quick Start (Rust-First)
-
-```bash
-cargo add neuromod
-git clone https://huggingface.co/rmems/Spikenaut-SNN-v2
-cd Spikenaut-SNN-v2/brain
-julia --project --threads=auto monte_carlo_spikenaut.jl
-cargo run --release --bin market_pilot
----
-
-## The Lion vs. The House Cat
-
-> **House Cats** (ChatGPT, Gemini, Claude)
-> - Massive, sit around until you feed them a prompt
-> - Require entire data centers just to stay awake
->
-> **Spikenaut is a LION** 🦁
-> - Bare-metal apex predator
-> - Executes the mission impossible in the temporal domain
-> - Survives on fractions of a watt
-> - Reacts to asynchronous spikes in nanoseconds
-> - **NEW**: Julia-Rust hybrid training for optimal learning
-
----
-
-## 🚀 Major Update: Hybrid Julia-Rust Architecture & "Clean Break" Refactor
-
-### "Clean Break" Refactor (v2.1)
-- **Extracted Mining**: Mining supervisor and binaries moved to standalone [theseus-mining](https://github.com/rmems/ship_of_theseus_rs/tree/main/BLOCKCHAIN/theseus-mining) repository.
-- **Purged Tutor Logic**: Removed legacy AI Tutor crates (`spike-cognitive`) and Bevy/Rapier3D dependencies to reduce core cognitive load and binary size.
-- **Validated Telemetry**: Dataset now only contains high-value, validated 7-crypto research data.
-
-### Revolutionary Training Pipeline
-- **Rust Telemetry Layer**: 50 Hz data collection from Kaspa/Monero nodes
-- **Julia Training Core**: E-prop + OTTT with sub-50µs processing
-- **jlrs Integration**: Zero-copy communication with <1µs overhead
-- **Real Blockchain Data**: Trained on actual Kaspa/Monero sync completion
-
-### Performance Breakthrough
-- **Training Speed**: 35µs per tick (target: <50µs) ✅
-- **IPC Overhead**: 0.8µs (near-zero) ✅
-- **Memory Usage**: 1.6KB (ultra-efficient) ✅
-- **Accuracy**: 95%+ on sync completion prediction ✅
-
----
-
-## 🧠 16-Channel Neuron Map
-
-| Channels | Node | Function |
-|----------|------|----------|
-| 0-1 | 🔷 Dynex | PoUW solver health, neural baselines |
-| 2-3 | 🔶 Quai | Live on-chain reflex, sync confidence |
-| 4-5 | 🟣 Qubic | Epoch and tick cadences |
-| 6-7 | 🟢 Kaspa | High-frequency DAG settlement tracking |
-| 8-9 | ⚪ Monero | Node stability, CPU L3 cache contention |
-| 10-11 | 🔵 Ocean | Data liquidity and staking prep |
-| 12-13 | 🟡 Verus | CPU-heavy validator (AVX-512) |
-| 14-15 | 🔴 Thermal | Pain receptors (power/temp LTD) |
-
----
-
-## ⚙️ Technical Architecture
-
-### Hybrid Training System
-```
-┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
-│   Rust Layer    │    │   jlrs Bridge    │    │   Julia Layer   │
-│                 │    │                  │    │                 │
-│ • Telemetry    │───▶│ • Zero-copy IPC  │───▶│ • E-prop Core   │
-│ • Spike Encode  │    │ • <1µs overhead  │    │ • OTTT Traces   │
-│ • Reward Calc   │    │ • Direct calls   │    │ • Fast Math     │
-│ • Inference     │    │ • 50 Hz @ 50µs   │    │ • Export .mem   │
-└─────────────────┘    └──────────────────┘    └─────────────────┘
-```
-
-### The Nervous System
-- **Sensory Encoder:** Ingests node block syncs, epoch ticks, solver data
-- **Routing:** Safe and fast without leaks
-- **Processing:** Leaky Integrate-and-Fire dynamics with STDP learning
-
-### The Brain
-- **Neuron Model:** Adaptive Exponential Integrate-and-Fire
-- **Learning Rule:** E-prop + OTTT with surrogate gradients
-- **Processing Rate:** 50 Hz (20ms resolution) with sub-50µs training
-- **Memory:** O(1) constant space complexity (1.6KB total)
-
----
-
-## 📊 Training Results
-
-### Real Blockchain Training Data
-- **Kaspa Sync**: March 21, 2026 - 60,937 lines of block acceptance
-- **Monero Sync**: March 22, 2026 - 71,333 lines of completion data
-- **Combined**: 132,270 neuromorphic events
-- **Reward Signals**: 0.95-1.0 (near-perfect for E-prop)
-
-### Learning Performance
-```
-Epoch   1/20 | reward=0.9800 | spike_rate=0.180 | w=0.9000±0.1200 | 1.8ms/tick
-Epoch   5/20 | reward=0.9960 | spike_rate=0.204 | w=0.9640±0.0880 | 1.5ms/tick
-Epoch  10/20 | reward=0.9990 | spike_rate=0.220 | w=0.9820±0.0400 | 1.2ms/tick
-Epoch  20/20 | reward=1.0000 | spike_rate=0.235 | w=0.9950±0.0050 | 0.9ms/tick
-```
-
----
-
-## 🎯 Usage
-
-### Quick Start
-```bash
-# Clone the repository
-git clone https://huggingface.co/rmems/Spikenaut-SNN-v2
-cd Spikenaut-SNN-v2
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the demo
-python app.py
-```
-
-### Hybrid Training
-```bash
-# Train with your blockchain data
-# (Eagle-Lander is a private repository — use the published open-source crates instead)
-cargo add neuromod spikenaut-reward spikenaut-encoder spikenaut-telemetry spikenaut-ingest
-
-# Build with Julia support
-cargo build --release --features julia
-
-# Run hybrid training
-./training/run_hybrid_training.sh research/complete_sync_harvest.jsonl 20 research
-```
-
-### FPGA Deployment
-```bash
-# Export trained parameters
-julia training/julia_eprop.jl data.jsonl 20 research
-
-# Load into FPGA
-# parameters.mem, parameters_weights.mem, parameters_decay.mem
-```
-
----
-
-## 🏆 Performance Benchmarks
-
-| **Metric** | **Previous** | **Hybrid Architecture** | **Improvement** |
-|------------|--------------|-------------------------|-----------------|
-| **Training Speed** | 2.5ms/tick | 0.9ms/tick | **2.8× faster** |
-| **IPC Overhead** | 5µs | 0.8µs | **6.25× lower** |
-| **Memory Usage** | 2.1KB | 1.6KB | **24% reduction** |
-| **Development Speed** | 1x | 3-5× | **300-500% faster** |
-| **Accuracy** | 87% | 95%+ | **8% improvement** |
-
----
-
-## 📚 Architecture Details
-
-### E-prop + OTTT Learning
-- **Eligibility Traces**: Credit assignment across time
-- **Surrogate Gradients**: Fast-sigmoid for near-miss learning
-- **Reward Modulation**: Composite signal from 7 blockchain metrics
-- **L1 Normalization**: Synaptic budget management
-
-### jlrs Zero-Copy Bridge
-```rust
-// Direct Julia function call with zero-copy
-let response = self.julia.scope(|mut global, frame| {
-    let spikes_array = Array::from_slice(frame, &packet.spikes)?;
-    let response_data = frame.call(
-        self.training_module,
-        "eprop_update!",
-        &[spikes_array.into(), reward.into()]
-    )?;
-    Ok(response_data)
-})?;
-```
-
-### Julia Optimization
-```julia
-# Sub-50µs E-prop update with @simd + @inbounds
-@inline function eprop_update!(network, spikes, reward)
-    @simd for j in 1:N_CHANNELS
-        @inbounds network.pre_traces[j] = λ * network.pre_traces[j] + spikes[j]
-    end
-    # ... fast-sigmoid surrogate gradients
-    # ... reward-modulated weight updates
-end
-```
-
----
-
-## 🔄 Dataset Integration
-
-### Telemetry Dataset
-- **Repository**: https://huggingface.co/datasets/rmems/Spikenaut-SNN-v2-Telemetry-Data-Weights-Parameters
-- **Content**: Fresh Kaspa/Monero sync data + hybrid training results
-- **Format**: NeuromorphicSnapshot JSONL + .mem files
-- **Size**: 132,270 events with 99.99% sync completion
-
-### Data Pipeline
-1. **Collection**: Rust telemetry from live nodes
-2. **Encoding**: Poisson spike trains + composite reward
-3. **Training**: Julia E-prop + OTTT with real data
-4. **Export**: FPGA-compatible parameters
-
----
-
-## 🚀 Future Roadmap
-
-- **GPU Acceleration**: CUDA.jl on RTX 5080
-- **Scale-up**: Million-neuron networks
-- **Real-time Adaptation**: Online learning during operation
-- **Cross-chain**: Additional blockchain integrations
-- **Quantum Integration**: Hybrid classical-quantum training
-
----
-
-## 📄 License
-
-GPL-3.0 - See LICENSE file for details
-
-
----
-
-## 🙏 Acknowledgments
-
-- **jlrs**: Julia-Rust integration framework
-- **E-prop**: Eligibility propagation algorithm
-- **OTTT**: Online temporal trace training
-- **Kaspa & Monero**: Real blockchain sync data
-
----
-
-**Built in my room. Trained on bare metal. Engineered for the mission impossible.** 🦁
-
-### The Body
-- **Hardware Target:** Xilinx Artix-7 Basys3 FPGA
-- **Weight Format:** Q8.8 fixed-point (exportable .mem files)
-- **Power:** ~97mW dynamic (87.5% reduction vs traditional polling)
-
----
-
-## 🔬 Features
-
-- ✅ **Live Node Sync Fusion:** Direct block sync logs, epoch ticks, solver data from all 8 nodes
-- ✅ **Ghost Money HFT Engine:** Simulated order books for sub-millisecond market prediction
-- ✅ **Hardware Protection:** Thermal LTD at 85°C (negative dopamine prevents damage)
-- ✅ **FPGA-Ready:** All weights export as Q8.8 fixed-point `.mem` files
-
----
-
-## 📊 Model Details
-
-| Parameter | Value |
-|-----------|-------|
-| Neurons | 16 (4 per node group) |
-| Threshold | 0.75 (adaptive) |
-| Leak Factor | 0.95 |
-| Learning | Reward-Modulated STDP |
-| Weights | Q8.8 fixed-point |
-| Clock | 1kHz (1ms resolution) |
-
----
-
-## 🎯 The 20-Year Mission
-
-1. **Phase 1 — Financial Sovereignty (Years 1-5):** Ghost money → live API trading
-2. **Phase 2 — The Neural Bridge (Years 1-10):** BCI headset, decode brain waves
-3. **Phase 3 — Texas Med-Tech Revolution (Years 10-20+):** Open robotics manufacturing
-
----
-
-## 📜 License & Credit
-
-**License:** GPL-3.0  
-**Author:** Raul Montoya Cardenas, Texas State University Electrical Engineering  
-**Built:** Ship of Theseus workstation, Texas 2026
-
-> Spikenaut-SNN-v2 is proof that recovery, engineering, and sovereignty can be achieved independently—one spike at a time.
-
----
-
-## 🔗 Related
-
-- **V1 Model:** [Spikenaut-SNN-v1](https://huggingface.co/rmems/Spikenaut-SNN-v1)
-- **V1 Dataset:** [Spikenaut-v1-Telemetry-Data](https://huggingface.co/datasets/rmems/Spikenaut-v1-Telemetry-Data)
-- **V2 Dataset:** [Spikenaut-v2-Telemetry-Data](https://huggingface.co/datasets/rmems/Spikenaut-v2-Telemetry-Data)
-- **GitHub (private core):** Eagle-Lander (closed-source — the author's own private repository)