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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting
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Neural Modeling of Episodic Memory: Encoding, Retrieval, and Forgetting

机译：情景记忆的神经建模：编码，检索和遗忘

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摘要

This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion adaptive resonance theory (ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs a continuous parallel search of stored episodic traces. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, where the proposed episodic memory model is evaluated based on the memory consumption for encoding events and episodes as well as recall accuracy using partial and erroneous cues. Our experimental results show that: 1) the model produces highly robust performance in encoding and recalling events and episodes even with incomplete and noisy cues; 2) the model provides enhanced performance in a noisy environment due to the process of forgetting; and 3) compared with prior models of spatio-temporal memory, our model shows a higher tolerance toward noise and errors in the retrieval cues.
机译：本文提出了一种神经模型，该模型可以根据连续不断的感官输入和从环境接收的反馈来学习情节轨迹。该模型基于融合自适应共振理论（ART）网络，通过动态创建认知节点来提取关键事件并编码事件之间的时空关系。该模型还包含一种新颖的内存搜索程序，该程序执行存储的情节轨迹的连续并行搜索。结合逐渐遗忘的机制，该模型能够实现高水平的内存性能和鲁棒性，同时控制随时间变化的内存消耗。我们目前进行的实验研究是根据内存消耗对事件和情节进行编码以及使用部分和错误提示进行回忆的准确性来评估拟议的情景记忆模型。我们的实验结果表明：1）即使在提示和提示不完整的情况下，该模型在编码和调用事件和情节方面也具有很高的鲁棒性； 2）由于遗忘的过程，该模型在嘈杂的环境中提供了增强的性能； 3）与先前的时空记忆模型相比，我们的模型对检索线索中的噪声和错误具有更高的容忍度。

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