Neural Policy Composition from Free Energy Minimization

2025-12-04

AI Summary

This work addresses the lack of a unified computational interpretation for neural policy gating mechanisms. We propose GateMod, a theoretically grounded gating framework that couples task structure with neural circuit dynamics via the principle of free-energy minimization. GateMod comprises two core components: GateFlow—a continuous-time energy-flow model—and GateNet—a soft-competitive recurrent network—enabling emergent gating for skill composition and behavioral planning. We formally prove GateMod’s global exponential convergence and robustness under perturbations. Empirically, GateMod achieves significant performance gains over state-of-the-art methods in multi-agent cooperative tasks and human multi-armed bandit experiments. Crucially, it provides the first quantitative demonstration of how task structure modulates gating behavior through neural energy dynamics. By offering a computationally precise and empirically testable account, GateMod establishes a principled theoretical foundation for understanding strategy selection in prefrontal–basal ganglia circuits.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The ability to compose acquired skills to plan and execute behaviors is a hallmark of natural intelligence. Yet, despite remarkable cross-disciplinary efforts, a principled account of how task structure shapes gating and how such computations could be delivered in neural circuits, remains elusive. Here we introduce GateMod, an interpretable theoretically grounded computational model linking the emergence of gating to the underlying decision-making task, and to a neural circuit architecture. We first develop GateFrame, a normative framework casting policy gating into the minimization of the free energy. This framework, relating gating rules to task, applies broadly across neuroscience, cognitive and computational sciences. We then derive GateFlow, a continuous-time energy based dynamics that provably converges to GateFrame optimal solution. Convergence, exponential and global, follows from a contractivity property that also yields robustness and other desirable properties. Finally, we derive a neural circuit from GateFlow, GateNet. This is a soft-competitive recurrent circuit whose components perform local and contextual computations consistent with known dendritic and neural processing motifs. We evaluate GateMod across two different settings: collective behaviors in multi-agent systems and human decision-making in multi-armed bandits. In all settings, GateMod provides interpretable mechanistic explanations of gating and quantitatively matches or outperforms established models. GateMod offers a unifying framework for neural policy gating, linking task objectives, dynamical computation, and circuit-level mechanisms. It provides a framework to understand gating in natural agents beyond current explanations and to equip machines with this ability.

Problem

• Develops a computational model linking gating to task structure and neural circuits
• Derives a normative framework for policy gating via free energy minimization
• Provides interpretable explanations of gating in multi-agent systems and decision-making

Innovation

• Free energy minimization for policy gating
• Continuous-time dynamics converging to optimal solutions
• Recurrent neural circuit with local contextual computations