Zhu Qing, Wang Rubin, Wang Ziyin
Institute for Cognitive Neurodynamics, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
Behav Brain Res. 2013 Nov 1;256:128-39. doi: 10.1016/j.bbr.2013.05.050. Epub 2013 Jun 5.
The rodent hippocampus has been used to represent the spatial environment as a cognitive map. Classical theories suggest that the cognitive map is a consequence of assignment of different spatial regions to variant cell populations in the framework of rate coding. The current study constructs a novel computational neural model of the cognitive map based on firing rate coding, as widely appears in associative memory, thus providing an explanation for formation and function of the two types of cognitive maps: the spatial vector map, responsible for self localization and simultaneous updating of detailed information; and the goal-oriented vector map, important in route finding. A proposed intermediate between these two map types was constructed by combining the spatial vector and goal-orientation maps to form an effective and efficient path finding mechanism. Application of such novel cognitive map based path finding methods to a mental exploration model was explored. With adaptation as a driving force, the basic knowledge of the location relationships in the spatial cognitive map was reformed and sent to the goal-oriented cognitive map, thus solving a series of new path problems through mental exploration. This method allows for rapid identification of suitable paths under variant conditions, thus providing a simpler and safer resource for path finding. Additionally, this method also provides an improved basis for potential robotic path finding applications.
啮齿动物的海马体已被用于将空间环境表征为一种认知地图。经典理论认为，认知地图是在速率编码框架下将不同空间区域分配给不同细胞群的结果。当前的研究基于广泛出现在联想记忆中的发放率编码构建了一种新型的认知地图计算神经模型，从而为两种类型的认知地图的形成和功能提供了解释：空间向量地图，负责自我定位和详细信息的同步更新；以及目标导向向量地图，在路径寻找中很重要。通过将空间向量地图和目标导向地图相结合，构建了这两种地图类型之间的一种中间形式，以形成一种有效且高效的路径寻找机制。探索了将这种基于新型认知地图的路径寻找方法应用于一种心理探索模型。以适应为驱动力，空间认知地图中位置关系的基础知识被重新构建并发送到目标导向认知地图，从而通过心理探索解决了一系列新的路径问题。这种方法允许在不同条件下快速识别合适的路径，从而为路径寻找提供了一种更简单、更安全的资源。此外，这种方法还为潜在的机器人路径寻找应用提供了更好的基础。
