Yan Chuankui, Wang Rubin, Qu Jingyi, Chen Guanrong
Department of Mathematics, School of Science, Hangzhou Normal University, Hangzhou, China.
Institute for Cognitive Neurodynamics, School of Science, East China University of Science and Technology, Shanghai, China.
Cogn Neurodyn. 2016 Aug;10(4):353-60. doi: 10.1007/s11571-016-9384-2. Epub 2016 Mar 26.
Extensive experiments on rats have shown that environmental cues play an important role in goal locating and navigation. Major studies about locating and navigation are carried out based only on place cells. Nevertheless, it is known that navigation may also rely on grid cells. Therefore, we model locating and navigation based on both, thus developing a novel grid-cell model, from which firing fields of grid cells can be obtained. We found a continuous-time dynamic system to describe learning and direction selection. In our simulation experiment, according to the results from physiology experiments, we successfully rebuild place fields of place cells and firing fields of grid cells. We analyzed the factors affecting the locating accuracy. Results show that the learning rate, firing threshold and cell number can influence the outcomes from various tasks. We used our system model to perform a goal navigation task and showed that paths that are changed for every run in one experiment converged to a stable one after several runs.
在大鼠身上进行的大量实验表明，环境线索在目标定位和导航中起着重要作用。关于定位和导航的主要研究仅基于位置细胞展开。然而，众所周知，导航也可能依赖于网格细胞。因此，我们基于两者对定位和导航进行建模，从而开发出一种新颖的网格细胞模型，从中可以获得网格细胞的放电场。我们发现了一个连续时间动态系统来描述学习和方向选择。在我们的模拟实验中，根据生理学实验结果，我们成功重建了位置细胞的位置场和网格细胞的放电场。我们分析了影响定位精度的因素。结果表明，学习率、放电阈值和细胞数量会影响各种任务的结果。我们使用我们的系统模型执行了一个目标导航任务，结果表明，在一个实验中每次运行都会改变的路径在几次运行后会收敛到一条稳定的路径。
