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.

连续学习是指代理的能力，即能够从持续的数据流中学习并在不遗忘旧信息的情况下传递知识。本文提出环境丰富可以作为研究前向传递的生物模型，启发人类化人工智能的发展。通过提供丰富的环境刺激，动物能够展现出显著的学习速度和新任务表现的改善，这在人类中被称为 “认知储备”。我们探讨了环境丰富后的解剖学、分子学和神经变化，并讨论了如何利用人工神经网络预测丰富的体验后神经计算的变化。最后，我们提供了一种将神经科学和人工智能研究相结合的方法，为快速高效的新任务学习能力的人工智能的发展开辟了道路。