1. Introduction
This proposal outlines a conceptual framework for building an artificial intelligence system composed of multiple specialized neural networks, each approximating the functional role of a specific mouse brain region. The goal is not to replicate biological structures, but to explore how modularity, specialization, and coordinated interaction can give rise to coherent and adaptive behavior.
The revised version incorporates an additional idea: a central coordinating neural network acting as an analogue of a simplified nervous system. This coordinating module regulates communication between functional modules and modulates the reward system, enabling more flexible and biologically inspired control.
2. General Objective
To design an artificial intelligence architecture composed of:
- Independent functional modules, each trained to approximate a specific cognitive or behavioral function inspired by mouse brain regions.
- A central coordinating network, responsible for regulating inter‑module communication and dynamically adjusting reward signals.
The global behavior of the system emerges from the interaction between these components.
3. Functional Modules
3.1. Independent Specialized Networks
Each module is a neural network dedicated to a particular function, such as:
- sensory processing
- spatial memory
- reward evaluation
- action selection
- basic decision‑making
These modules are trained independently and before the coordinating system. Their goal is to discover functional analogues of biological regions, not to mimic their structure.
3.2. Training Strategy for Modules
Each module undergoes iterative training to approximate the functional behavior of its target region. Once trained, modules are frozen or semi‑frozen, ensuring stability during later stages.
This staged approach avoids the instability that arises when all components learn simultaneously.
4. Central Coordinating Network (“Artificial Nervous System”)
4.1. Purpose
Instead of relying on a fixed communication protocol, the system includes a neural network that:
- receives summarized states from all modules
- determines which modules should influence behavior at each moment
- regulates the flow of information between modules
- dynamically modulates reward signals
This network acts as a functional analogue of a simplified nervous system, integrating signals and shaping global behavior.
4.2. Training Strategy
The coordinating network is trained after the functional modules are stable. Its learning objective is to:
- coordinate modules efficiently
- maintain system stability
- distribute rewards in a way that promotes coherent behavior
- resolve conflicts between modules
- adapt to the environment through experience
This staged training significantly reduces complexity and improves stability.
5. Interaction Between Modules and Coordinator
5.1. Communication
Modules send compact representations of their internal states to the coordinator. The coordinator decides:
- which modules should be prioritized
- how information should flow
- when to inhibit or amplify signals
5.2. Reward Modulation
Instead of a global, static reward function, the coordinator adjusts reward signals dynamically, inspired by biological systems such as dopaminergic pathways.
This allows the system to adapt its learning priorities based on context.
6. Advantages of the Revised Architecture
6.1. Increased Stability
Training modules first and the coordinator last avoids chaotic interactions during learning.
6.2. Greater Biological Plausibility
The architecture mirrors how evolution provides pre‑configured functions, while experience shapes coordination and reward modulation.
6.3. Flexibility and Adaptation
A learned coordinator can adjust communication patterns and reward distribution in ways that fixed protocols cannot.
6.4. Clearer Functional Interpretability
Each module has a defined purpose, and the coordinator’s role is conceptually transparent.
7. Remaining Challenges
Although the revised architecture simplifies many aspects, several challenges remain:
- defining appropriate representations for module‑to‑coordinator communication
- ensuring the coordinator does not learn pathological strategies
- designing environments that encourage stable and meaningful coordination
- developing metrics to evaluate global behavior and inter‑module cooperation
- scaling the architecture to more complex tasks
These challenges are more manageable and scientifically interesting than those in the original formulation.
8. Conclusion
This revised proposal presents a modular artificial intelligence architecture inspired by the functional organization of the mouse brain. Independent functional modules approximate specific cognitive roles, while a central coordinating network—trained last—regulates communication and reward dynamics. This approach combines biological inspiration with computational practicality, offering a promising direction for exploring emergent behavior, modular cognition, and interpretable AI systems.
Author’s Note
I am not an expert or academic in computational neuroscience. My intention is simply to share this conceptual idea in case it may inspire discussion or be of interest to researchers in the field.