No Need for a Cognitive Map: Decentralized Memory for Insect Navigation

Abstract

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. When desert ants search for food, they often have to travel over long distances, more then ten thousand times their body lengths and then turn back to find the nest entrance. It is known from many experiments that these animals employ a skylight compass including the sun, a pedometer, and a mechanism called path integration. This means that during walking they continuously update the vector pointing from their actual position back to the nest site. In addition they use landmarks. However, based on observations of the behaviour of ants and honey bees several authors have argued that these animals finally employ a neural system that is able to represent frequently visited locations in the form of a map (a “cognitive map”). Having a map-like system available would allow the animal to find a shortcut between two separately learned locations without having learned this direct path between both locations beforehand. As such shortcuts have been observed, cognitive maps have been assumed to exist. Here we show in a simulation study based on artificial neural networks that shortcuts as observed in the experiments are also possible with a memory system using a completely decentralized architecture not including an explicit cognitive map.