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Replay and compositional computation
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DOI：10.1016/j.neuron.2022.12.028.png)
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Abstract
En 中文
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 hu-man ability to radically generalize past experience to solve new problems.
Keywords:
HIPPOCAMPAL PLACE CELLS
NEURAL-NETWORKS
PROBABILISTIC MODELS
PATTERN SEPARATION
EPISODIC MEMORY
TIME CELLS
SEQUENCES
REPRESENTATIONS
CONNECTIONIST
SPACE
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