Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer

TL;DR

This paper explores how environmental enrichment in animals can serve as a biological model for forward transfer in continual learning, aiming to inspire AI systems that learn new tasks rapidly by leveraging prior knowledge.

Contribution

It introduces environmental enrichment as a biological analogy for forward transfer and discusses how neuroscience insights can inform AI development for improved continual learning.

Findings

Enriched animals show faster learning and better performance on new tasks.

Neural and molecular changes post-enrichment correlate with improved learning.

Proposes using ANN models to predict neural changes after enrichment.

Abstract

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of data and transfer knowledge without forgetting old information. One crucial aspect of CL is forward transfer, i.e., improved and faster learning on a new task by leveraging information from prior knowledge. While this ability comes naturally to biological brains, it poses a significant challenge for artificial intelligence (AI). Here, we suggest that environmental enrichment (EE) can be used as a biological model for studying forward transfer, inspiring human-like AI development. EE refers to animal studies that enhance cognitive, social, motor, and sensory stimulation and is a model for what, in humans, is referred to as 'cognitive reserve'. Enriched animals show significant improvement in learning speed and performance on new tasks, typically exhibiting forward transfer. We explore anatomical, molecular, and neuronal changes post-EE and discuss how artificial neural networks (ANNs) can be used to predict neural computation changes after enriched experiences. Finally, we provide a synergistic way of combining neuroscience and AI research that paves the path toward developing AI capable of rapid and efficient new task learning.