Neural Network Model of Memory Retrieval.
Neural Network Model of Memory Retrieval.
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DOI：
10.3389/fncom.2015.00149
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发表时间：
2015
影响因子：
3.2
通讯作者：
Tsodyks M
中科院分区：
文献类型：
--
作者：
Recanatesi S;Katkov M;Romani S;Tsodyks M
人类的记忆可以存储大量的信息。然而，回忆往往是一项具有挑战性的任务。在经典的自由回忆范式中，参与者被要求重复一个简短的单词列表，人们会对最短到5个单词的列表犯错误。我们提出了一种基于Hopfield神经网络的记忆提取模型，其中项目之间的转换由其长期记忆表征的相似性来确定。模型的平均场分析揭示了对应于(1)单个记忆表示和(2)记忆表示之间的交集的网络的稳定状态。我们发现，在噪声存在的情况下，振荡反馈抑制导致这些状态之间的转换，从而触发不同记忆的提取。网络动力学定性地预测了回忆在实验中观察到的新记忆项目所需的时间间隔的分布。它表明，在其表征中神经元数量较多的项目在统计上更容易回忆，并揭示了我们提取记忆的能力可能存在的瓶颈。总体而言，我们提出了一个与实验观察广泛兼容的信息检索神经网络模型，并与我们最近的图形模型(Romani等人，)一致。
Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al.,).