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Abstract：
In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.
摘要：
在各种物种和行为环境中，学习和记忆形成招募了两个神经系统，随着时间的推移，一个系统中的初始可塑性被巩固到另一个系统中。此外，众所周知，合并是有选择性的；也就是说，有些经验比其他经验更有可能被巩固成长期记忆。这里,我们提出并分析了一个模型，该模型捕获了此类现象背后的常见计算原理。该模型的关键组成部分是一种机制，通过该机制，长期学习和记忆系统优先考虑与短期系统的先前更新一致的突触变化的存储。这个机制,我们称之为召回门控合并，具有屏蔽长期记忆免受假突触变化的作用，使其能够专注于环境中的可靠信号。我们针对不同类型的学习问题描述了该模型的神经电路实现，包括监督学习，强化学习，和自动关联内存存储。这些实现涉及突触可塑性规则，这些规则由预测精度、决策信心,或熟悉。然后，我们开发了模型的学习和记忆性能的分析理论，与仅依赖突触局部整合机制的替代方案相比。我们发现，召回式整合提供了显著的优势，基本上放大了信噪比，可以在嘈杂的环境中存储存储器。我们证明了召回门控整合会导致行为学习范式中存在的许多现象，包括间隔学习效果，与任务相关的合并率，以及短期和长期通路中不同的神经表现。
