Active rewiring
In insect navigation we often talk of active vision, where the behaviour of an individual can shape the sensory input that is received. A prime example of this comes during the learning walks/flights of insects as they begin foraging and learn about their environs. It turns out that during these periods, individuals are also engaged in a form of active rewiring. This is a review paper which captures lots of the recent excellent work from Wolfgang Rössler’s group with the exciting idea that they can look at development and learning processes in the insect brain for individuals that have been producing natural behaviour in a real world setting.
In the authors words: “Analyses of visual neuronal pathways to the central complex and mushroom bodies – two prominent sensory integration centers in the insect brain – revealed that first light exposure and visual experience during learning walks lead to distinct structural re-organizations of synaptic circuits in both brain centers reflecting initial calibrations and memory processes in the ants’ visual compass systems.”
Rössler, W. (2019). Neuroplasticity in desert ants (Hymenoptera: Formicidae) – importance for the ontogeny of navigation. Myrmecological News, 29.
To the heavens … …
A couple of space themed nuggets here. Firstly, appearance based visual navigation models are tested for their potential for navigation of planetary rovers. Secondly, an opportunistic investigation of bee homing during a solar eclipse.
Grixa, I., Schulz, P., Stürzl, W., & Triebel, R. (2018, October). Appearance-Based Along-Route Localization for Planetary Missions. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6327-6334). IEEE.
Waiker, P., Baral, S., Kennedy, A., Bhatia, S., Rueppell, A., Le, K., … & Rueppell, O. (2019). Foraging and homing behavior of honey bees (Apis mellifera) during a total solar eclipse. The Science of Nature, 106(1-2), 4.
Computational economy through active vision
Minimal cognitive modelling is an approach to computational neuroscience that emerged from the Artificial Life community. The idea is that simple models that nevertheless underpin complex (intelligent) behaviours are existence proofs that agents need not have complex neuronal architectures for smart behaviours. In this paper, the authors look at the active vision strategies of bees and show how those behaviours mean that very simple computations could underpin basic numerousity.
Author Summary: “Varying levels of numerical cognition have been found in several animal species. Bees, in particular, have been argued to be able to count up to four items and solve complex numerical tasks. Here we present an exceedingly simple neural circuit that, when provided with the actual visual input that the bee is receiving while carrying out the task, can make reliable estimates on the number of items in the display. Thus we suggest that the elegance of numerical problem solving in bees might not lie in the formation of numerical concepts (such as ‘‘more,’’ ‘‘less,’’ or ‘‘zero’’), but in the use of specific flight movements to scan targets, which streamlines the visual input and so renders the task of counting computationally inexpensive. Careful examination of the actual inspection strategies used by animals might reveal that animals often employ active scanning behaviors as shortcuts to simplify complex visual pattern discrimination tasks.”
Vasas, V., & Chittka, L. (2018). Insect-inspired sequential inspection strategy enables an artificial network of four neurons to estimate numerosity. iScience.
From the sky to the brain
Abstract: “Many insects navigate by integrating the distances and directions travelled on an outward path, allowing direct return to the starting point. Fundamental to the reliability of this process is the use of a neural compass based on external celestial cues. Here we examine how such compass information could be reliably computed by the insect brain, given realistic constraints on the sky polarisation pattern and the insect eye sensor array. By processing the degree of polarisation in different directions for different parts of the sky, our model can directly estimate the solar azimuth and also infer the confidence of the estimate. We introduce a method to correct for tilting of the sensor array, as might be caused by travel over uneven terrain. We also show that the confidence can be used to approximate the change in sun position over time, allowing the compass to remain fixed with respect to ‘true north’ during long excursions. We demonstrate that the compass is robust to disturbances and can be effectively used as input to an existing neural model of insect path integration. We discuss the plausibility of our model to be mapped to known neural circuits, and to be implemented for robot navigation.”
Gkanias, E., Risse, B., Mangan, M., & Webb, B. (2018). From skylight input to behavioural output: a computational model of the insect polarised light compass. bioRxiv, 504597. doi: https://doi.org/10.1101/504597
Wild experiments
The ability to study complex behaviours in natural settings is one of the major plus points of the ant navigation model system. Taken to its extreme, it is possible to observe the fine details of every excursion made by an individual ant across her foraging life in environments where we can reconstruct many aspects of the information available. This review from Freas et al focusses on this feature of ant navigation and gives a thorough account of the recent advances that have been made in understanding the interactions between ant behaviour, learning and the environment. In their own words: “We call for more experimental ethology focussed on learning processes, both by exploring run-by-run and step-by-step acquisition of information for navigation, as well as for other natural tasks in an animal’s life.”
Freas, C. A., Fleischmann, P. N., & Cheng, K. (2018). Experimental Ethology of Learning in Desert Ants: Becoming Expert Navigators. Behavioural processes.
How landmarks might structure the learning of landmarks
We all know that after finding a new food source, bees and ants will produce a systematic looping behaviour, returning to the food often. These loops may well be involved in the learning of the properties of the food (size etc.) but might also be involved in the learning of environmental information. Here we can see how the presence of a landmark near a recently discovered food source structures the subsequent looping behaviour, with ants producing a tighter distribution. There are two (at least) possible reasons for this: It might be the presence of the landmark induces a learning walk type behaviour because the ant wants to learn this visual information; Or it might be that the presence of visual information allows a tight food search distribution because it provide the ant with accurate movement information.
Sakiyama, T. (2018). Emergence of a complex movement pattern in an unfamiliar food place by foraging ants. Journal of Comparative Physiology A, 1-6.
Make your own landmark
We talk a lot about the use of visual information for navigation, but surely life would be easier for navigating insects if they were able to create their own visual landmarks. One way in which this can happen is via nest structures, with one lovely example from ants being the thatched “chimneys” of Acromyrmex. Moreira et al. perform a series of manipulations to ask if these ants use such manufactured nest structures as visual or olfactory guidance cues. The evidence suggests that, in fact, they are visual cues.
Moreira, I. J. S., Santos, M. F., & Madureira, M. S. (2018). Why do Acromyrmex nests have thatched entrance structures? Evidence for use as a visual homing cue. Insectes Sociaux.
