Llofriu M, Tejera G, Contreras M, Pelc T, Fellous J M, Weitzenfeld A
University of South Florida, United States; Universidad de la Republica, Uruguay.
Universidad de la Republica, Uruguay.
Neural Netw. 2015 Dec;72:62-74. doi: 10.1016/j.neunet.2015.09.006. Epub 2015 Oct 19.
There has been extensive research in recent years on the multi-scale nature of hippocampal place cells and entorhinal grid cells encoding which led to many speculations on their role in spatial cognition. In this paper we focus on the multi-scale nature of place cells and how they contribute to faster learning during goal-oriented navigation when compared to a spatial cognition system composed of single scale place cells. The task consists of a circular arena with a fixed goal location, in which a robot is trained to find the shortest path to the goal after a number of learning trials. Synaptic connections are modified using a reinforcement learning paradigm adapted to the place cells multi-scale architecture. The model is evaluated in both simulation and physical robots. We find that larger scale and combined multi-scale representations favor goal-oriented navigation task learning.
近年来，针对海马体位置细胞和内嗅网格细胞编码的多尺度特性开展了广泛研究，引发了许多关于它们在空间认知中作用的推测。在本文中，我们聚焦于位置细胞的多尺度特性，以及与由单尺度位置细胞组成的空间认知系统相比，它们如何在目标导向导航过程中促进更快的学习。任务由一个具有固定目标位置的圆形场地组成，在其中一个机器人经过多次学习试验后被训练找到通往目标的最短路径。使用适应位置细胞多尺度架构的强化学习范式来修改突触连接。该模型在模拟和物理机器人中均进行了评估。我们发现更大尺度和组合的多尺度表征有利于目标导向导航任务学习。
