基于神经元协同激励的稳定时间可控情景记忆

Episodic Memory with Controllable Steady-state Period Using Coherent Spin-interaction Neural Networks

情景记忆(Episodic memory)被认为是学习与记忆、感觉信号的加工和处理等认知功能的一个重要表现。在大脑的运行过程中,模式的稳定时间是可变化的。为了更好的模仿真实大脑在情景记忆过程中的状态,提出了一种基于神经元协同激励的不同模式的稳定时间不同的情景记忆模型。在本模型当中,模式和输入模式之间的相似性控制模式的稳定时间,相似度和模式稳定时间成正相关。同时,神经元协同激励情景记忆存储容量比传统的情景记忆模型得到明显的提高,并且存储容量和网络规模成指数比例关系。

Episodic information processing, for instance the episodic memory, plays an important role on many functions of brain. In this study, a novel neural network for episodic memory with controllable steady-state period based on coherent spin-interaction was proposed. Owing to a new sampling function, the steady-state period can be controlled by scale parameter and the overlap between the input pattern and the stored patterns. Ascribing to the coherent spin-interaction, the episodic storage capacity can be enlarged compared with the existing episodic memory models. The results show that the episodic storage capacity has exponential relationship to the dimension of the neural network.