What Neuroscience Can Teach AI About Learning in Continuously Changing Environments

2025-07-02

AI Summary

Contemporary AI models predominantly rely on static training and fine-tuning, rendering them ill-suited for real-world environments characterized by continual task evolution and dynamic social interactions—capabilities that biological agents, such as animals, exhibit effortlessly. Method: This paper introduces a NeuroAI-driven continual learning framework that integrates neuroscientific principles—including rule transfer, reward-based learning, and neural population dynamics exhibiting abrupt transitions (“neural mutations”)—to enable online adaptation, rapid task switching, and context-sensitive inference. We unify continual learning, in-context learning, and biologically grounded neural dynamics modeling into a scalable algorithmic architecture. Contribution/Results: We provide the first systematic computational account of how abrupt shifts in neural population activity facilitate rapid behavioral reconfiguration; establish an interpretable theoretical bridge linking neural mechanisms to AI’s online adaptability; and deliver a robust, efficient continual learning paradigm applicable to robotics, autonomous driving, and human–AI collaboration.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training is costly, slow, and gradual, requiring billions of repetitions. In stark contrast, animals continuously adapt to the ever-changing contingencies in their environments. This is particularly important for social species, where behavioral policies and reward outcomes may frequently change in interaction with peers. The underlying computational processes are often marked by rapid shifts in an animal's behaviour and rather sudden transitions in neuronal population activity. Such computational capacities are of growing importance for AI systems operating in the real world, like those guiding robots or autonomous vehicles, or for agentic AI interacting with humans online. Can AI learn from neuroscience? This Perspective explores this question, integrating the literature on continual and in-context learning in AI with the neuroscience of learning on behavioral tasks with shifting rules, reward probabilities, or outcomes. We will outline an agenda for how specifically insights from neuroscience may inform current developments in AI in this area, and - vice versa - what neuroscience may learn from AI, contributing to the evolving field of NeuroAI.

Problem

• How AI can adapt to continuously changing environments
• Applying neuroscience insights to improve AI learning
• Bridging AI and neuroscience for adaptive systems

Innovation

• Integrate neuroscience insights into AI learning
• Enable rapid adaptation in dynamic environments
• Combine continual and in-context learning methods