| 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. |