The emergence of habitual routes

One of the interesting features of insect navigation systems is the way that innate guidance mechanisms (such as Path Integration or beacon aiming) interact with route learning. One such innate behaviour, which is essential in complex environments is obstacle avoidance, which is very rarely considered in accounts of route navigation. Bertrand et al. take a modelling approach, firstly looking at how a parsimonious model of obstacle avoidance can be built, but an interesting property of their testing is the emergence of habitual routes through clutter. Here is an extract from the author summary: “…. Inspired by the abilities of insects, we developed a parsimonious algorithm to avoid collisions in challenging environments solely based on elementary motion detectors. We coupled our algorithm to a goal direction and then tested it in cluttered environments. The trajectories resulting from this algorithm show interesting goal-directed behavior, such as the formation of a small number of routes, also observed in navigating insects.”

Bertrand, O. J., Lindemann, J. P., & Egelhaaf, M. (2015). A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology, 11(11).

Visual resolution and view-based navigation

For any organism there are increased metabolic costs that come with increased resolution within a sensory system.  This simple fact can precede a line of thought where one think there must be a trade-offs between the cost of increased sensory resolution and the inherent value that comes from a greater amount of information . In this paper we look at the performance of hypothetical agents performing view-based navigation. In simulation we vary the visual resolution of agents and measure the consequent navigation performance. The interesting thing is that agents with low resolution perform better than agents with high resolution when asked to recapitulate a visually defined route. This simple demo shows how the sensory systems of animals need not always be a trade-off of performance and utility.

Wystrach, A., Dewar, A., Philippides, A., & Graham, P. (2015). How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation. Journal of Comparative Physiology A, 1-9.

Straight lines from coloured skys

Over the last few years, the concerted work of the Lund vision group has given us a great insight into the mechanisms of orientation that allow the plucky little dung beetle to roll his ball in a straight line. These beetles are able to use a range of orientation information from the sky (sun, moon, polarisation, even the milky way) and now a new cue is shown to provide orientation information for beetles. The sky has a chromatic gradient and beetles are shown to use such a gradient in order to roll their dung balls in a straight line.

Basil el Jundi, James J. Foster, Marcus J. Byrne, Emily Baird, Marie Dacke (2015) Spectral information as an orientation cue in dung beetles. Biology Letters, doi: 10.1098/rsbl.2015.0656

Interactions between behaviour and learning

Here is another nice review from the new journal Current Opinions in Insect Science, which promises to be an interesting read.

Abstract: “Flower patterns are thought to influence foraging decisions of insect pollinators. However, the resolution of insect compound eyes is poor. Insects perceive flower patterns only from short distances when they initiate landings or search for reward on the flower. From further away flower displays jointly form larger-sized patterns within the visual scene that will guide the insect’s flight. Chromatic and achromatic cues in such patterns may help insects to find, approach and learn rewarded locations in a flower patch, bringing them close enough to individual flowers. Flight trajectories and the spatial resolution of chromatic and achromatic vision in insects determine the effectiveness of floral displays, and both need to be considered in studies of plant–pollinator communication.”

de Ibarra, N. H., Langridge, K. V., & Vorobyev, M. (2015). More than colour attraction: behavioural functions of flower patterns. Current Opinion in Insect Science.

Different brain areas for different types of learning

Just a short note on an interesting new paper from PNAS and whilst this is not strictly a navigation paper, it is certainly very relevant. Devaud et al have investigated the role of the insect mushroom bodies (MBs) in non elemental learning. So called higher order learning might involve learning that the components A and B are negatively reinforced, whilst their mixture (AB) is positively reinforced. Intact MBs were necessary for the higher order learning tasks, but not simpler ‘elemental’ learning. This suggests that different brain areas might be needed for different styles of learning. Something that resembles the organisation of the vertebrate brain.

Devaud, J. M., Papouin, T., Carcaud, J., Sandoz, J. C., Grünewald, B., & Giurfa, M. (2015). Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations. Proceedings of the National Academy of Sciences, 201508422.

Memories of home vectors

PI can be used by ants to guide homeward and foodward memories, however it is unclear how distance and direction information becomes part of the long term memories of ants. Fernandes et al trained wood ants to find food at a fixed distance along a channel, with training locations dissociated from visual cues. One can test for vector memories by taking fed ants directly to the channel and observing homing behaviour that isn’t driven directly by PI. In this test ants walk in an appropriate distance and direction, these vector memories are primed simply by being in a fed state. A further batch of ants were trained (on alternate trials) to two feeder distances in opposite directions. Fed ants directly placed in the channel will choose a direction randomly, but then travel the appropriate distance for that direction. Thus distance and direction components are bound into insulated memories.

A.S.D. Fernandes, A. Philippides, T.S. Collett and J.E. Niven (2015) The acquisition and expression of memories of distance and direction in navigating wood ants. J Exp Biol doi:10.1242/jeb.125443

Cue integration in ants

For the control of behaviour, agents must take information from multiple sources and somehow merge them or decide between them in order to optimally control behaviour or develop accurate knowledge of the state of the environment.  Navigating insects provide a nice model for asking questions of this type as we can put learned visual information at odds with directions from path integration. Matthew Collett showed that both of these information sources can drive ant navigation at the same time. However, how they are averaged is unclear. Wystrach et al, used a nice experimental trick to help pick apart the weighting of these information sources. An insect’s path integration system has an increasing error with increasing length of the outward journey. That is to say, the region of the world within which the origin of the route might be increases in size. However, the error in the angular component of path integration decreases with distance away from the nest. So after a long journey, an ant far from the nest will have a more accurate departure bearing (compared to a short journey) even though the positional error in Path Integration is greater than for the small journey. Observing ants (with different length home vectors) when there is a conflict between visual information and PI, shows that ants’ resultant headings take into account the variability in the estimate of direction from the PI system. So far, so good, though an extra condition leads to an intriguing further result. The weighting of the PI component depends not on the total journey length (as would be optimal) but the current length of the home vector and therefore it seems that ants have an economical heuristic when deciding how to weight different cues.

Wystrach, A., Mangan, M., & Webb, B. (2015, October). Optimal cue integration in ants. In Proc. R. Soc. B (Vol. 282, No. 1816, p. 20151484). The Royal Society.

Bumblebee saccades

Saccadic movements are a part of the visual behaviours of many animals. Here Boeddeker et al look at the saccades of navigating bumblebees, which seem to follow the classical pattern of fast head turns that allow animals to spend the maximum possible time in pure translation.

Abstract: “Changes in flight direction in flying insects are largely due to roll, yaw and pitch rotations of their body. Head orientation is stabilized for most of the time by counter rotation. Here, we use high-speed video to analyse head- and body-movements of the bumblebee Bombus terrestris while approaching and departing from a food source located between three landmarks in an indoor flight-arena. The flight paths consist of almost straight flight segments that are interspersed with rapid turns. These short and fast yaw turns (“saccades”) are usually accompanied by even faster head yaw turns that change gaze direction. Since a large part of image rotation is thereby reduced to brief instants of time, this behavioural pattern facilitates depth perception from visual motion parallax during the intersaccadic intervals. The detailed analysis of the fine structure of the bees’ head turning movements shows that the time course of single head saccades is very stereotypical. We find a consistent relationship between the duration, peak velocity and amplitude of saccadic head movements, which in its main characteristics resembles the so-called “saccadic main sequence” in humans. The fact that bumblebee head saccades are highly stereotyped as in humans, may hint at a common principle, where fast and precise motor control is used to reliably reduce the time during which the retinal images moves.”

Boeddeker, N., Mertes, M., Dittmar, L., & Egelhaaf, M. (2015). Bumblebee Homing: The Fine Structure of Head Turning Movements. PloS one, 10(9), e0135020.

Navigation and learning in the insect brain

The two most commonly discussed brain areas in insect navigation studies are the Mushroom Bodies and the Central Complex. Recent work with Drosophila have given us amazing insights into the architecture of these areas. However there will, no doubt, be many differences in the organisation and function of these brain areas between flies, ants and bees. Two papers in the latest issue of Current Opinion in Insect Science review the latest findings regarding these brain areas.

Farris, S. M., & Van Dyke, J. W. (2015). Evolution and function of the insect mushroom bodies: contributions from comparative and model systems studies.Current Opinion in Insect Science.

Plath, J. A., & Barron, A. B. (2015). Current progress in understanding the functions of the insect central complex. Current Opinion in Insect Science.

Neural coding for visual navigation

In the Drosophila central brain, the ellipsoid body contains different populations of so-called Ring Neurons. Different groups of Ring Neurons are necessary for different visually guided behaviours. Recently published data describe the receptive fields for two classes of these cells. What is interesting is that these cells have very large receptive fields and are very small in number, suggesting that each sub-population of cells might be a bottleneck in the processing of visual information for a specific behaviour. It has recently been shown how R1 ring neurons are necessary for place learning in Drosophila. However, the receptive fields of these neurons are yet to be described. By examining the information provided by different populations of hypothetical visual neurons in simulations of experimental arenas, we show that neurons with ring neuron-like receptive fields are sufficient for defining a location visually. In this way we provide a link between the type of information conveyed by ring neurons, in general, and the behaviour they support.

Dewar, A. D., Wystrach, A., Graham, P., & Philippides, A. (2015). Navigation-specific neural coding in the visual system of Drosophila. Biosystems.