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----
-license: mit
-tags:
-- brain-inspired
-- spiking-neural-network
-- multi-task-learning
-- continual-learning
-- modular-ai
-- biologically-plausible
----
-
-# ModularBrainAgent 🧠
-**Author:** Aliyu Lawan Halliru (`@Almusawee`)  
-**Affiliation:** Independent AI Researcher (Nigeria)  
-**License:** MIT  
-**Paper:** [Download PDF](./ModularBrainAgent_Paper.pdf)  
-**Diagram:** (Coming soon)
-
----
-
-## 🧠 Abstract
-
-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.
-
-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.
-
-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.
-
----
-
-## 📌 Architecture Overview
-
-- Spiking sensory neurons for input encoding  
-- Attention-based relay for signal routing  
-- Adaptive interneuron logic for abstraction  
-- Neuroendocrine modulation (gain control)  
-- GRU-based recurrent loop (autonomic memory)  
-- Mirror comparator for goal-state reflection  
-- Replay buffer with task tagging  
-- Multimodal encoders and task heads
-
----
-
-## 🤝 License
-MIT License (free to use, adapt, and build upon with attribution)
-
-## 📝 Citation
-
-
-> ⚠️ **Note**: This version of the model is a **working prototype**.  
-> While the architecture is complete and documented,  
-> training and module testing are ongoing. Contributions welcome.