Free recall scaling laws and short-term memory effects in a latching attractor network.
Free recall scaling laws and short-term memory effects in a latching attractor network.
复制标题
锁定吸引子网络中的自由回忆缩放定律和短期记忆效应。
DOI：
10.1073/pnas.2026092118
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发表时间：
2021
影响因子：
11.1
通讯作者：
Boboeva V
中科院分区：
文献类型：
--
作者：
Boboeva V
尽管人类记忆的复杂性，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。虽然抽象的随机矩阵模型可以解释这些规律，但到目前为止，它们在相互作用的神经元的大型网络中实现的可能性仍然没有得到充分的探索。研究了具有放电率自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，网络从一个记忆到另一个记忆的转换行为受到限制环的约束，限制环阻止网络召回所有记忆，其扩展性与实验中发现的相似。当该模型补充了异联想学习规则，补充了标准的自联想学习规则，以及短期突触促进，我们的模型再现了其他关键的发现，在自由回忆文献，即，序列位置效应，邻接和向前不对称效应，语义效应发现，以指导记忆回忆。该模型与一系列广泛的操作一致，旨在更好地了解影响回忆的变量，例如排练的作用、呈现率以及连续和/或列表末尾干扰因素条件。我们预测，回忆能力可能会增加与添加少量的噪音，例如，在回忆过程中的弱随机刺激的形式。最后，我们预测，尽管编码记忆的统计数据对回忆能力有很大的影响，但回忆能力的幂律仍然是成立的。
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.