---
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)

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## 🧠 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.

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## 📌 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

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## 🤝 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.