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What Neuroscience Can Teach AI About Learning in Continuously Changing Environments.
Arxiv
|September 19, 2025
Summary
Animals continuously adapt to changing environments, unlike current AI models trained once. This perspective explores how neuroscience can inform AI
Area of Science:
- Neuroscience and Artificial Intelligence (AI)
- Computational Neuroscience
- Machine Learning
Background:
- Modern AI models, including large language models, undergo costly, one-time training on massive datasets, resulting in fixed parameters.
- Animals exhibit remarkable adaptability, continuously learning and adjusting to dynamic environmental conditions, especially in social contexts.
- This adaptive capacity in animals is characterized by rapid behavioral shifts and sudden changes in neural population activity.
Purpose of the Study:
- To explore the potential for AI to learn from neuroscience, particularly in the domain of continual learning and adaptation.
- To bridge the gap between AI's static learning paradigms and the dynamic, adaptive learning observed in animal behavior.
- To foster a synergistic relationship where neuroscience informs AI development and AI tools advance neuroscience research.
Main Methods:
- Integration of literature on continual and in-context learning in AI.
- Review of neuroscience research on animal learning in tasks with shifting rules and reward probabilities.
- Comparative analysis of computational processes underlying learning in AI and animal models.
Main Results:
- Identified a significant contrast between AI's static training and animals' continuous adaptation.
- Highlighted the growing importance of adaptive AI for real-world applications like robotics and human-AI interaction.
- Established a framework for mutual learning between AI and neuroscience.
Conclusions:
- Neuroscience offers valuable insights for developing more adaptive and continually learning AI systems.
- AI advancements can provide novel tools and perspectives for understanding biological learning mechanisms.
- This interdisciplinary approach is crucial for the advancement of the emerging field of NeuroAI.
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