Emergent spatial goals in an integrative model of the insect central complex
Fig 5
(A) Navigation using PI and vector memory, replicating [20]. a. The steering circuit in this simulation is only composed of the hΔ pathway, supporting path integration. When the agent is lacking food (food = 0) it promotes the exploration behaviour through the excitation of a specific FBt. When food is found, the DAN circuit triggers memory formation. The motivation then switches to the return state (food = 1) where the FBt is inhibited, leading to homing behaviour. b. Sequence of behaviour implemented: (1) the agent leaves the nest to explore (pre-determined zig-zag pattern). (2) When the agent reach 200l.u. from the nest, it is provided with a food reward, triggering the formation of the memory while simultaneously switching the motivation to the return mode. (3) Homing behaviour to the nest (catchment area of 20l.u. diameter) c. Reset of the motivation to the exploration mode (food=0). This time, the behaviour of the agent is left under the control of the steering circuit which should bring it back to the memorised location. See results in Fig 6. (B) Navigation using visual guidance and vector memory, replicating and extending [26]. a. For visual navigation without PI the steering circuit is only composed of the PFN to PFL pathway. The DAN reward circuit receives inputs from the sum of the target-detection ommatidia activity (see B.b), so the circuit forms a vector memory of its direction when facing the target. See results in Fig 7. b. Visual circuit used to control the recognition of a green object in the central visual field (Fig B in S1 Text). c. For visual navigation with PI, the circuit includes both PFN and hΔ pathways to the PFL. The agent thus forms a memory of both the direction of the visual target and the location from which it was seen, further improving its ability to locate the target. See results in Fig 8. Clipart(s) in the figure have been modified from https://openclipart.org/.