Memory States and Transitions between Them in Attractor Neural Networks
Memory States and Transitions between Them in Attractor Neural Networks
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DOI：
10.1162/neco_a_00998
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发表时间：
2017-10-01
影响因子：
2.9
通讯作者：
Tsodyks, Misha
中科院分区：
文献类型：
--
作者：
Recanatesi, Stefano;Katkov, Mikhail;Tsodyks, Misha
人类的记忆能够检索与刚刚检索的记忆相似的记忆。这种联想能力是我们日常信息处理的基础。目前的记忆模型还不能支持大脑可以用来积极利用记忆之间的相似性的机制。目前的想法是，为了在吸引子神经网络中诱导转换，有必要消除当前记忆。我们引入了一种能够诱导记忆之间转换的新型机制，其中神经动力学积极利用记忆之间的相似性来检索新的记忆。对多种记忆具有选择性的神经元群体通过自身成为吸引子而在这一机制中发挥着至关重要的作用。该机制基于神经网络控制兴奋-抑制平衡的能力。
Human memory is capable of retrieving similar memories to a just retrieved one. This associative ability is at the base of our everyday processing of information. Current models of memory have not been able to underpin the mechanism that the brain could use in order to actively exploit similarities between memories. The current idea is that to induce transitions in attractor neural networks, it is necessary to extinguish the current memory. We introduce a novel mechanism capable of inducing transitions between memories where similarities between memories are actively exploited by the neural dynamics to retrieve a new memory. Populations of neurons that are selective for multiple memories play a crucial role in this mechanism by becoming attractors on their own. The mechanism is based on the ability of the neural network to control the excitation-inhibition balance.