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-Spikenaut-SNN-v2 🚀
-Welcome to the second iteration of Spikenaut, a Spiking Neural Network (SNN) project exploring the intersection of neuromorphic computing and efficient AI logic.
-The "Clean Slate" Update
-If you’re looking for the previous datasets or model weights—they’re gone.
-To be honest, the initial data was messy. After a lot of scrubbing and late nights, I realized that building on a shaky foundation isn't the way to move forward in Electrical Engineering or Data Science. I made the tough call to delete everything and start fresh.
-Current Status
-[x] Data Purge: Completed (Removing the "unclean" legacy data).
-[ ] Data Re-Ingestion: In Progress (Implementing new cleaning pipelines).
-[ ] Architecture Refinement: Ongoing.
-About the Author
-I'm Raul, an Electrical Engineering student at TXST. I’m a first-gen student navigating the complexities of ADHD and engineering. This project is a reflection of my journey: sometimes you have to tear it all down to build it back stronger.
-Stay tuned. The data will be cleaner, the logic will be sharper, and the results will be worth the wait.
+Gemini said
+Spikenaut-SNN-v2: Neuromorphic Reservoir & Hybrid Architectures
+⚠️ Research Status: Ground Zero Rebuild (April 2026)
+I am currently taking this repository through a complete architectural reset. Following my initial prototype phase, I have moved back to a "Ground Zero" state to ensure absolute verification of every spiking neuron, synapse, and temporal parameter.
+
+I realized I was moving too fast after my first successful prototype. To maintain the integrity of my research, I am now proceeding at a steadier, more deliberate pace. My current focus is centered on verifying telemetry integrity and ensuring my hardware-software baseline is bulletproof before scaling back into complex hybrid systems.
+
+🧠 Model & Research Description
+Spikenaut-SNN-v2 is my research-grade Spiking Neural Network (SNN) model designed for high-frequency data processing and autonomous decision-making in noisy environments.
+
+Core Research Objectives:
+Hardware-Driven Learning: Utilizing real-world telemetry from hardware—specifically my Dynex mining baseline—to drive STDP (Spike-Timing-Dependent Plasticity).
+
+Hybridization (SpikeLMo): Investigating the infusion of SNN logic into the OLMoE-7B (Mixture of Experts) architecture. I am exploring the use of SNNs as low-power, temporal "Neuromorphic Routers" to gate high-level LLM experts.
+
+HFT Logic: Developing specialized 16-neuron Liquid State Machine (LSM) reservoirs tuned for high-frequency trading market micro-structures.
+
+🏗 System & Infrastructure
+This research is developed and trained locally on my Ship of Theseus workstation running Fedora 43. My codebase is organized under the Eagle-Lander framework.
+
+Primary Logic: Powered by my neuromod Rust crate.
+
+Methodology: "Measure twice, spike once." I have moved away from bulk AI-assisted data uploads to a manual, deterministic verification process to eliminate noisy or "bad" data.
+
+Hardware Integration: My Electrical Engineering background in Micro and Nano devices informs how I bridge this software with physical hardware, such as the Digilent Basys 3 FPGA.
+
+🛠 Project Roadmap
+[x] Initial Prototype (Dynex SNN)
+
+[ ] CURRENT: Telemetry verification and baseline stabilization.
+
+[ ] 16-neuron LSM reservoir tuning for HFT data.
+
+[ ] Prototype integration with OLMoE-7B MoE layers.
+
+📜 License
+This model and its associated logic are released under the GNU General Public License v3.0 (GPL-3.0).
+
+This documentation and research summary were drafted by Gemini, a large language model built by Google, based on the specific research parameters and project history provided by Raul Montoya Cardenas