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
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.