Simulating Biological Intelligence: Active Inference with Experiment-Informed Generative Model

2025-08-09

AI Summary

This study investigates the computational foundations of goal-directed behavior in autonomous agents to advance the development of safe, interpretable, and biologically plausible intelligent systems. Method: Grounded in active inference theory, we propose an embodied generative modeling framework that integrates empirical neuronal data. The model embeds perception–action loops within a hierarchical generative architecture, explicitly representing memory-dependent learning and prospective predictive planning. It is validated through alignment with both simulated game environments and electrophysiological recordings. Contribution/Results: Our model successfully recapitulates neuronal-level decision dynamics observed in biological systems. Quantitatively, it reveals synergistic interactions between memory updating and future-state prediction during behavioral choice. By bridging mechanistic neuroscientific principles with scalable AI architectures, the framework provides an interpretable, neurobiologically grounded, and computationally tractable paradigm for unifying computational neuroscience and artificial intelligence.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

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

Abstract

With recent and rapid advancements in artificial intelligence (AI), understanding the foundation of purposeful behaviour in autonomous agents is crucial for developing safe and efficient systems. While artificial neural networks have dominated the path to AI, recent studies are exploring the potential of biologically based systems, such as networks of living biological neuronal networks. Along with promises of high power and data efficiency, these systems may also inform more explainable and biologically plausible models. In this work, we propose a framework rooted in active inference, a general theory of behaviour, to model decision-making in embodied agents. Using experiment-informed generative models, we simulate decision-making processes in a simulated game-play environment, mirroring experimental setups that use biological neurons. Our results demonstrate learning in these agents, providing insights into the role of memory-based learning and predictive planning in intelligent decision-making. This work contributes to the growing field of explainable AI by offering a biologically grounded and scalable approach to understanding purposeful behaviour in agents.

Problem

• Modeling decision-making in biologically inspired autonomous agents
• Exploring active inference for explainable AI systems
• Simulating learning with experiment-informed generative models

Innovation

• Active inference for decision-making modeling
• Experiment-informed generative models simulation
• Memory-based learning and predictive planning