Abstract
Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control—enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.
Highlights
Social insects, including ants and bees, have evolved remarkable behavioral capabilities for navigating in complex dynamic environments, which enable them to survive by finding vital locations
Goal-Directed Navigation in Insect-Inspired Artificial Agents between their nest and reliable food sources (Collett, 2012; Mangan and Webb, 2012; Collett and Cardé, 2014; Cheng et al, 2014). These navigational behaviors rely on sensory information, mainly from visual cues, and on internal memories acquired through learning mechanisms (Collett et al, 2013). Such learned memories have shown to be based on orientation directing vectors, which are generated by a process called path integration (PI) (Wehner, 2003)
We will further demonstrate that the generated behaviors resemble insect navigational strategies, but can predict certain observed behavioral parameters of social insects
Summary
Social insects, including ants and bees, have evolved remarkable behavioral capabilities for navigating in complex dynamic environments, which enable them to survive by finding vital locations (e.g., food sources). Goal-Directed Navigation in Insect-Inspired Artificial Agents between their nest and reliable food sources (Collett, 2012; Mangan and Webb, 2012; Collett and Cardé, 2014; Cheng et al, 2014) These navigational behaviors rely on sensory information, mainly from visual cues, and on internal memories acquired through learning mechanisms (Collett et al, 2013). In PI, animals integrate angular and linear ego-motion cues over time to produce an estimate of their current location with respect to their starting point This vector representation is called the home vector (HV) and is used by social insects to return back to the home on a straight path. After returning from a successful foraging run, insects re-apply this vector information in subsequent foraging runs (Capaldi et al, 2000; Wolf et al, 2012; Fernandes et al, 2015)
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