Unlike any other group of animals, all ant species are social: individual ants share the food they gather with their nestmates and as a consequence they must repeatedly leave their nest to find food and then return home with it. These back-and-forth foraging trips have been studied for about a century and much of our growing understanding of the strategies underlying animal navigation has come from these studies. One important strategy that ants use to keep track of where they are on a foraging trip is ‘path integration’, in which they continuously update a ‘home vector’ that gives their estimated distance and direction from the nest. As path integration accumulates errors, it cannot be relied on to bring ants precisely home: such precision is accomplished by using views of the nest acquired before they start foraging. Further learning is scaffolded by home vectors or remembered food vectors, which guide a route and help in learning useful views experienced on the way. Many species rely on olfaction as well as vision for route guidance and the full details of their foraging paths have revealed how ants use a mix of innate and learnt multisensory cues. Wood ants, a species on which we focus in this review, take an oscillating path along a pheromone trail to sample odours, but acquire visual information only at the peaks and troughs of the oscillations. To provide a working model of the neural basis of the multimodal navigational strategies of ants, we outline the anatomy and functioning of major central brain areas and neural circuits — the central complex, mushroom bodies and lateral accessory lobes — that are involved in the coordination of navigational behaviour and the learning of visual and olfactory patterns. Because ant brains have not yet been well-studied, we rely on the work that has been done with other species — notably, Drosophila, silkworm moths and bees — to derive plausible neural circuitry that can deliver the ants’ navigational strategies.
Funding
ActiveAI - active learning and selective attention for robust, transparent and efficient AI : EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL | EP/S030964/1
Emergent embodied cognition in shallow, biological and artificial, neural networks : BBSRC-BIOTECHNOLOGY & BIOLOGICAL SCIENCES RESEARCH COUNCIL | BB/X01343X/1