论文标题
重播和组成计算
Replay and compositional computation
论文作者
论文摘要
在大脑中的重播已被视为排练，或者最近被视为从过渡模型中采样。在这里，我们提出了一个新的假设：重播能够实现一种组成计算的形式，其中实体被组装成相关结合的结构以得出定性的新知识。这个想法建立在神经科学的最新进展上，这表明海马弹性地将对象绑定到可概括的角色，并将这些角色结合的对象重现为复合语句。我们建议实验来检验我们的假设，并通过注意对缺乏人类从根本上概括过去的经验来解决新问题的AI系统的影响。
Replay in the brain has been viewed as rehearsal, or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally-bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems.
