Pyramidal Hybrid Neural Network Framework (BrainIAc_v1.0) : Technical Documentation and Experimental Results
Pyramidal Hybrid Neural Network Framework (BrainIAc_v1.0) : Technical Documentation and Experimental Results
Project Overview Pre-publication technical documentation and experimental validation results for a hybrid neural network framework enabling gradient-based training of heterogeneous spiking neuron populations. Description/Summary: Key Features: Mixed neuron training: LIF + Izhikevich (10 types) + Hodgkin-Huxley (3 variants) trained together via gradient descent Large-scale validation: Up to 200 neurons with realistic cortical architectures Three learning modes: Separate, Simultaneous, Combined (pretrain + fine-tune) Pyramidal architecture: 1→50→1 hourglass structure (13 layers, 308 neurons) Real-time 3D visualization: 60 FPS interactive rendering with OpenGL Dynamic reconfiguration: Neurons can change type during simulation Comprehensive testing: 20+ validation scenarios, 75% success rate Validated Results: XOR problem: 56% loss reduction (pure LIF, 100 epochs) Mixed types: Stable convergence (4 LIF + 4 Izh + 4 HH, 80 epochs) ⭐ BREAKTHROUGH 100-neuron cortical microcircuit: Functional layer specialization validated 200-neuron cortical circuit: Realistic excitatory/inhibitory dynamics 13 neuron types: All show expected biological firing patterns Comprehensive Dataset: Technical documentation: 24 PDFs (~150 pages total) Core theory (9 PDFs) Framework architecture (5 PDFs) Advanced topics (10 PDFs: biological layers, visualization, roadmap) Experimental validation: 65+ CSV files with raw spike timing data Visual documentation: 67 screenshots Analysis: RESULTS_SUMMARY.md (36.8 KB detailed analysis) README.md (28.4 KB): Complete usage instructions and technical specifications CITATION.md (11.3 KB): Citation formats in BibTeX, APA, IEEE, MLA, Nature LICENSE.txt (2.4 KB): Full CC BY-NC 4.0 license terms License: CC BY-NC 4.0 (Non-Commercial) ✅ Academic research and educational use freely permitted with proper attribution ❌ Commercial use NOT permitted without explicit written permission 📧 For commercial licensing inquiries: theo.vallois@hotmail.fr Source Code: Full source code (BrainIAc framework) will be released under CC BY-NC 4.0 on GitHub upon paper acceptance (expected Q1 2026). Repository: https://github.com/EmpireStrikesBack/NeuroModel Demonstration Video: https://youtu.be/kiU609iozFw Real-time 3D visualization of 308-neuron pyramidal network with interactive controls. Contact: Author: Théo Vallois Email: theo.vallois@hotmail.fr GitHub: https://github.com/EmpireStrikesBack/NeuroModel
brain-inspired AI, low-power AI, surrogate gradient, pyramidal architecture, real-time visualization, biological plausibility, neuromorphic computing, izhikevich neurons, energy-efficient computing, Hodgkin-Huxley neurons, Computational neuroscience, LIF neurons, spiking neural networks, hybrid neural networks, gradient descent
brain-inspired AI, low-power AI, surrogate gradient, pyramidal architecture, real-time visualization, biological plausibility, neuromorphic computing, izhikevich neurons, energy-efficient computing, Hodgkin-Huxley neurons, Computational neuroscience, LIF neurons, spiking neural networks, hybrid neural networks, gradient descent
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