Catastrophic forgetting in connectionist networks

Area of Science:

• Cognitive Science
• Artificial Intelligence
• Neuroscience

Background:

• Human cognition exhibits gradual forgetting, not catastrophic loss, of old information when new information is acquired.
• Artificial neural networks, particularly distributed connectionist models, are prone to catastrophic forgetting, where new learning erases old information.
• The generalization and robustness of neural networks are paradoxically linked to their susceptibility to catastrophic forgetting.

Purpose of the Study:

• To examine the causes and consequences of catastrophic forgetting in neural networks.
• To review existing and potential solutions to mitigate catastrophic forgetting.
• To explore how biological cognitive systems overcome this issue and apply insights to artificial networks.

Main Methods:

• Literature review of catastrophic forgetting in neural networks.
• Analysis of the mechanisms underlying catastrophic forgetting.
• Exploration of neurobiological principles relevant to memory retention.

Main Results:

• Catastrophic forgetting in neural networks stems from the same features enabling their powerful learning capabilities.
• Numerous solutions exist, but a universally effective method remains an active research area.
• The human brain employs strategies that prevent catastrophic forgetting, offering potential models for artificial systems.

Conclusions:

• Understanding catastrophic forgetting is crucial for developing more robust and human-like artificial intelligence.
• Inspiration from biological memory systems can guide the development of neural networks that retain information effectively.
• Future research should focus on integrating brain-inspired mechanisms to overcome catastrophic forgetting in artificial neural networks.