What Neuroscience Can Teach AI About Learning in Continuously Changing Environments.

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.