| 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. |
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| 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. |
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| 🧠 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. |
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| Core Research Objectives: |
| Hardware-Driven Learning: Utilizing real-world telemetry from hardware—specifically my Dynex mining baseline—to drive STDP (Spike-Timing-Dependent Plasticity). |
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| 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. |
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| HFT Logic: Developing specialized 16-neuron Liquid State Machine (LSM) reservoirs tuned for high-frequency trading market micro-structures. |
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| 🏗 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. |
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| Primary Logic: Powered by my neuromod Rust crate. |
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| 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. |
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| Hardware Integration: The Digilent Basys 3 FPGA. |
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| 🛠 Project Roadmap |
| [x] Initial Prototype (Dynex SNN) |
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| [ ] CURRENT: Telemetry verification and baseline stabilization. |
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| [ ] 16-neuron LSM reservoir tuning for HFT data. |
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| [ ] Prototype integration with OLMoE-7B MoE layers. |
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| 📜 License |
| This model and its associated logic are released under the GNU General Public License v3.0 (GPL-3.0). |
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| 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 |
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