在线记忆绑定任务的工作记忆：一个混合模型
Working Memory for Online Memory Binding Tasks: A Hybrid Model
摘要：
工作记忆是大脑中保存和处理在线信息的模块。在这项工作中，我们设计了一个混合模型，其中一个简单的前馈网络耦合到一个平衡的随机网络通过一个读写向量称为接口向量。三种情况下，他们的结果进行了讨论类似的n-back任务，一阶记忆绑定任务，广义一阶记忆任务，二阶记忆绑定任务。重要的结果是，我们的工作记忆的双组件模型表现出良好的性能与学习仅限于前馈组件。在这里，我们利用了随机网络的特性，而无需学习。最后，一个更复杂的内存绑定任务称为，一个基于线索的内存绑定任务，其中的线索是作为输入表示的绑定关系，提示网络选择有用的内存块。据我们所知，这是第一次随机网络作为一个灵活的记忆被证明在在线绑定任务中发挥重要作用。我们可以将我们的结果解释为工作记忆的候选模型，其中前馈网络学习与临时存储随机网络交互，作为注意力控制执行系统。
英文摘要：
Working Memory is the brain module that holds and manipulates information online. In this work, we design a hybrid model in which a simple feed-forward network is coupled to a balanced random network via a read-write vector called the interface vector. Three cases and their results are discussed similar to the n-back task called, first-order memory binding task, generalized first-order memory task, and second-order memory binding task. The important result is that our dual-component model of working memory shows good performance with learning restricted to the feed-forward component only. Here we take advantage of the random network property without learning. Finally, a more complex memory binding task called, a cue-based memory binding task, is introduced in which a cue is given as input representing a binding relation that prompts the network to choose the useful chunk of memory. To our knowledge, this is the first time that random networks as a flexible memory is shown to play an important role in online binding tasks. We may interpret our results as a candidate model of working memory in which the feed-forward network learns to interact with the temporary storage random network as an attentional-controlling executive system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
