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首页> 外文期刊>PLoS Computational Biology >Solving Navigational Uncertainty Using Grid Cells on Robots
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Solving Navigational Uncertainty Using Grid Cells on Robots

机译:在机器人上使用网格单元解决导航不确定性

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To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments.
机译:为了成功地导航栖息地,许多哺乳动物使用了两种机制的结合:路径整合和使用地标进行标定,它们共同使它们能够估计其位置和方向或姿势。在大型自然环境中,这两种机制都具有不确定性:路径整合过程会累积误差,而界标校准会受到感知模糊性的限制。尚不清楚在存在这种不确定性的情况下动物如何形成连贯的空间表示。使用机器人的导航研究已经确定,可以通过维护机器人姿势的多个概率估计来有效解决不确定性。在这里,我们展示了使用基于大鼠的基于大脑的机器人导航系统RatSLAM,背尾内侧内嗅皮层(dMEC)中的结膜网格细胞如何保持姿势的多个估计。基于啮齿动物的空间响应细胞和功能工程原理,RatSLAM计算模型的核心细胞与啮齿动物的网格细胞具有相似的特征,我们通过复制精髓的Moser实验来证明这一点。我们将RatSLAM模型应用于一种新的实验范式,该范式旨在在存在感知歧义的情况下检查机器人或动物的响应。我们的计算方法使我们能够观察多个位置假设的短期总体编码,这种现象在啮齿动物记录中很难观察到。我们目前提供的行为和神经证据表明,联合网格单元可以维持并传播姿势的多个估计值,即使没有唯一识别的提示,也可以随着时间解决正确的姿势估计。尽管最近的研究集中在网格状的放电特性,准确性和网格单元的表示能力上,但我们的结果确定了联合网格单元在过滤感官不确定性方面可能发挥的关键和独特作用。我们预计我们的研究将成为动物实验的起点,该实验将在感知上模棱两可的环境中测试导航。

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