The robust navigation of insects arises from the coordinated action of concurrently functioning and interacting guidance systems. Computational models of specific brain regions can account for isolated behaviours such as path integration or route following but the neural mechanisms by which their outputs are coordinated remains unknown. Here we take a functional modelling approach to identify and model the elemental guidance subsystems required by homing insects before producing realistic adaptive behaviours by integrating their outputs in a biologically constrained unified model mapped onto identified neural circuits. Homing paths are quantitatively and qualitatively compared with real ant data in a series of simulation studies replicating key infield experiments. Our analysis reveals that insects require independent visual homing and route following capabilities which we show can be realised by encoding panoramic skylines in the frequency domain, using image processing circuits in the optic lobe and learning pathways through the Mushroom Bodies and Anterior Optic Tubercle respectively before converging in the Central Complex steering circuit. Further we demonstrate that a ring-attractor network inspired by firing patterns recorded in the Central Complex can optimally integrate the outputs of path integration and visual homing systems guiding simulated ants back to their familiar route, and a simple non-linear weighting function driven by the output of the Mushroom Bodies provides a context-dependent switch allowing route following strategies to dominate and the learned route retraced back to the nest when familiar terrain is encountered. The outcome is a biologically realistic neural model capable of reproducing an array of adaptive homing behaviours in realistic environments through the combined action of the Central Complex and the Mushroom Bodies neuropils forwarding the case for a distributed architecture of the insect navigational toolkit.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)