Biological Cybernetics, and Center for Excellence CITEC, University of Bielefeld, Bielefeld, Germany.
PLoS Comput Biol. 2011 Mar;7(3):e1002009. doi: 10.1371/journal.pcbi.1002009. Epub 2011 Mar 17.
In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a 'map', a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.
在许多动物中，长距离导航的能力是觅食的重要前提。例如，人们普遍认为沙漠蚂蚁和蜜蜂，以及哺乳动物，利用路径整合来找到返回栖息地的路。然而，动物是否能够获得和使用所谓的认知地图，这是一个长期存在争议的问题。这种“地图”是对觅食区域的全局空间表示，通常被认为可以让动物在两个地点之间找到捷径，尽管它们之前从未走过这条直接的路线。我们采用人工神经网络方法，开发了一个基于路径整合和各种地标引导机制的人工记忆系统（一组独立的地标定义的记忆元素）。个体记忆元素的激活取决于一个独立的动机网络和一个（部分）不对称的侧抑制网络。关于主体的绝对位置的信息是存在的，但存在于一个单独的记忆中，该记忆只能被路径整合子系统用于控制行为，而不能与系统的其他记忆元素一起用于计算目的。因此，在这个模拟中，不存在认知地图的神经基础。然而，由这个网络控制的主体能够完成各种被认为依赖于认知地图的导航任务，如蚂蚁和蜜蜂所表现出来的行为。例如，蜜蜂中观察到的类似地图的行为是从分散的系统中涌现出来的特性。因此，这种行为可以在不假设存在认知地图（即对觅食空间的一致表示）的情况下得到解释。我们假设，所提出的网络本质上存在于昆虫大脑的蘑菇体中。
