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license: mit |
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tags: |
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- brain-inspired |
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- spiking-neural-network |
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- multi-task-learning |
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- continual-learning |
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- modular-ai |
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- biologically-plausible |
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--- |
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# ModularBrainAgent 🧠 |
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**Author:** Aliyu Lawan Halliru (`@Almusawee`) |
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**Affiliation:** Independent AI Researcher (Nigeria) |
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**License:** MIT |
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**Paper:** [Download PDF](./ModularBrainAgent_Paper.pdf) |
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**Diagram:** (Coming soon) |
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--- |
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## 🧠 Abstract |
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We propose ModularBrainAgent, a biologically motivated neural architecture for multi-task learning that mirrors the functional organization of the human brain. Unlike monolithic deep networks, our model is designed with architectural intelligence: distinct modular subsystems that reflect perceptual, attentional, memory, and decision-making pathways in biological cognition. |
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Each component — including spiking sensory processors, adaptive interneurons, relay routing layers, neuroendocrine gain modulators, recurrent autonomic loops, and mirror-state comparators — serves a unique cognitive function. These modules are not just trainable; they are structurally positioned to enable learning itself. This built-in cognitive topology improves sample efficiency, interpretability, and continual adaptability. |
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The model supports multimodal input via GRUs, CNNs, and shared encoders, and leverages a task-specific replay buffer for lifelong learning. Experimental design favors generalization across domains and tasks with minimal interference. We argue that structural cognition — not just data or gradient optimization — is the key to general-purpose artificial intelligence. ModularBrainAgent provides a functional and extensible blueprint for biologically plausible, task-flexible, and memory-capable AI systems. |
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--- |
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## 📌 Architecture Overview |
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- Spiking sensory neurons for input encoding |
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- Attention-based relay for signal routing |
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- Adaptive interneuron logic for abstraction |
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- Neuroendocrine modulation (gain control) |
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- GRU-based recurrent loop (autonomic memory) |
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- Mirror comparator for goal-state reflection |
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- Replay buffer with task tagging |
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- Multimodal encoders and task heads |
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--- |
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## 🤝 License |
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MIT License (free to use, adapt, and build upon with attribution) |
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## 📝 Citation |
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> ⚠️ **Note**: This version of the model is a **working prototype**. |
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> While the architecture is complete and documented, |
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> training and module testing are ongoing. Contributions welcome. |
