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FAMILIARITY GATED LEARNING FOR INFERENTIAL USE OF EPISODIC MEMORIES IN NOVEL SITUATIONS - A ROBOT SIMULATION

机译:熟悉的门控学习,以便在新颖的情况下推论eoiodic回忆的推论 - 机器人仿真

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摘要

This paper presents a computational model for encoding and inferential reuse of memories, based on novelty and familiarity principle. The method is strongly inspired by the state of the art understanding of the hippocampal functioning and especially its role in novelty detection and episodic memory formation in relation to spatial context. A navigation task is used to provide an experimental setup for behavioral testing with a rat-like agent. The model is build on three presumptions. First that episodic memory formation has behavioral, as well as sensory and perceptual correlates; second, hippocampal involvement in the novelty/ familiarity detection and episodic memory formation, experimentally supported by neurobiological experiments; and third, that a straightforward parallel exists between internal hippocampal and an abstract spatial representations. Some simulation results are shown to support the reasoning and reveal the methods applicability for practically oriented behavioral simulation.
机译:本文基于新奇和熟悉原理,提出了一种用于编码和推动回收的计算模型。该方法受到对海马功能的理解的最新理解的强烈启发,特别是其在新颖性检测中的作用和相对于空间环境的开缩记忆形成。导航任务用于提供具有大鼠样剂的行为测试的实验设置。该模型是在三个推定上建立的。首先,情节内存形成具有行为,以及感官和感知相关;二,海马参与新颖性/熟悉性检测和显口记忆形成,通过神经生物学实验进行实验支持;第三,内部海马和抽象空间表示之间存在简单的并行。一些仿真结果显示出支持推理并揭示用于实际上面向行为模拟的方法的应用。

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