Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained underexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behavior of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely, serial position effects, contiguity and forward asymmetry effects, and the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates, and continuous and/or end-of-list distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example, in the form of weak random stimuli during recall. Finally, we predict that, although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

尽管人类记忆很复杂，但像自由回忆这样的范式已经揭示了强大的定性和定量特征，例如控制回忆能力的幂律。尽管抽象的随机矩阵模型可以解释这些定律，但迄今为止，它们在相互作用的神经元的大型网络中实现的可能性仍未得到充分探索。我们研究了一种具有放电率适应和全局抑制的长时记忆吸引子网络模型。在适当的条件下，网络从记忆到记忆的转换行为受到限制循环的限制，该循环阻止网络调用所有记忆，其缩放比例类似于实验中发现的。当模型补充了异联想学习规则，补充了标准的自动联想学习规则，除了短期突触促进外，我们的模型重现了自由回忆文献中的其他关键发现，即连续位置效应、连续性和前向不对称效应，以及指导记忆回忆的语义效应。该模型与一系列旨在更好地理解影响回忆的变量的操作一致，例如排练的作用、演示率以及连续和/或列表末尾的干扰条件。我们预测召回能力可能会随着少量噪声的增加而增加，例如，在召回期间以弱随机刺激的形式。最后，我们预测，尽管编码记忆的统计数据对召回能力有很强的影响，但控制召回能力的幂律仍有望成立。