Learning offline: memory replay in biological and artificial reinforcement learning

Area of Science:

• Neuroscience
• Artificial Intelligence
• Machine Learning
• Cognitive Science

Background:

• Reinforcement learning (RL) is a key brain function for reward-based decision-making.
• RL is widely applied in machine learning and AI for optimizing decision-making.
• Replay, or the reactivation of past experiences, is a common mechanism in both biological and artificial RL.

Purpose of the Study:

• To review recent advancements in understanding the functional roles of replay in neuroscience and AI.
• To explore how replay supports learning processes like generalization and continual learning.
• To identify opportunities for knowledge transfer between neuroscience and AI to advance learning and memory research.

Main Methods:

• Literature review of recent developments in replay mechanisms.
• Comparative analysis of replay functions in biological neural networks and deep neural networks.
• Synthesis of findings to highlight complementary progress and potential synergies.

Main Results:

• Replay is vital for memory consolidation in biological systems.
• Replay is essential for stabilizing learning in artificial deep neural networks.
• Replay mechanisms show potential for enhancing generalization and continual learning in both domains.

Conclusions:

• Replay plays a fundamental role in optimizing learning and memory across biological and artificial systems.
• Understanding replay in AI can inform neuroscience, and vice versa, leading to novel insights.
• Further interdisciplinary research on replay can accelerate advancements in artificial and biological learning.