A lateralised design for the interaction of visual memories and heading representations in navigating ants

Abstract

The navigational skills of ants, bees and wasps represent one of the most baffling examples of the powers of minuscule brains. Insects store long-term memories of the visual scenes they experience, and they use compass cues to build a robust representation of directions. We know reasonably well how long-term memories are formed, in a brain area called the Mushroom Bodies (MB), as well as how heading representations are formed in another brain area called the Central Complex (CX). However, how such memories and heading representations interact to produce powerful navigational behaviours remains unclear. Here we combine behavioural experiments with computational modelling that is strictly based on connectomic data to provide a new perspective on how navigation might be orchestrated in these insects. Our results reveal a lateralised design, where signals about whether to turn left or right are segregated in the left and right hemispheres, respectively. Furthermore, we show that guidance is a two-stage process: the recognition of visual memories, presumably in the MBs, does not directly drive the motor command, but instead updates a desired heading, presumably in the CX, which in turn is used to control guidance using celestial compass information. Overall, this circuit enables ants to recognise views independently of their body orientation, and combines terrestrial and celestial cues in a way that produces exceptionally robust navigation.

A unified mechanism for innate and learned visual landmark guidance in the insect central complex

“…The aim of the weight modulation is to enable the formation of a similar spatial weight pattern as presented in the previous section. The choice of a synaptic memory, instead of a memory accumulated in the FBn subset [21; 41], is primarily motivated by the permanent modification that could define an innate preference for vertical bars [3; 7]. However, we note that either solution would lead to the same mathematical formulation given the abstracted neural and synapse models used here.…”

“…This is achieved thanks to the segmentation of these neurons into two unilateral subsets controlling independently turns on each side. This organisation corresponds to a more basic, probably ancestral, control scheme called tropotaxis [37] where the left-right comparison of a sensory signal drives the course to take [44]. We show here how this left-right structure of the CX output could be co-opted by sensory pathways (visual attraction and MB long-term memory) that do not themselves have such a left-right structure, through an adaptively acquired distribution of weights between the EB compass bump and the PFL3 neurons.…”

“…This is achieved by (i) the division of the PFL3 neurons into two unilateral subsets independently controlling turns towards each side [38] and (ii) the unbalanced connectivity scheme between the compass neurons (EPGs) and these unilateral subsets (figure 4). This organisation corresponds to a more basic, perhaps ancestral, control scheme called tropotaxis [41] in which the left-right comparison of a sensory signal controls the orientation taken [51]. In addition, the control of steering by a continuous probabilistic comparison of left and right turns naturally produces oscillatory or zigzag pattern that resembles paths observed in insects following pheromone trails, odor plumes [52] and/or visual guidance [9; 53; 54].…”

“…Therefore, the CX could act as a smoothing filter, in addition to direct taxis mechanisms, to generate a short-term memory of the sensory world when this appears to be unsteady. Note that these advantages also apply to enhance either menotaxis or memory-guided orientation over a direct taxis system, e.g., smoothing the integration of the MB signals that can be unsteady [37]. The integration of a sparsely accessible sensory signals could be particularly interesting considering cluttered and unsteady environments where insects can forage.…”

“…Finally, a key component of the model is represented by the reward signal extracted from sensory pathways. The FB represents an ideal target to model the influence of parallel sensory pathways on the CX [37] because it receives inputs from several other brain areas, including the MB [51][52][53], while sharing the columnar organisation observed in the EB and the PB [14]. For example, Drosophila FB has been shown necessary to the control of the steering in a visual pattern recognition paradigm [54], involving the EB [55].…”