COVID catch-up: Reviews
Suffice to say 2020 has been a weird year and unfortunately the Insect Navigation BLOG has suffered in the covid turmoil. So here are some themed catch-up posts and normal service will resume for articles released from Aug 2020 onwards.
Review Articles
Kheradmand, B., & Nieh, J. C. (2019). The Role of Landscapes and Landmarks in Bee Navigation: A Review. Insects, 10(10), 342.
Hulse, B. K., & Jayaraman, V. (2020). Mechanisms Underlying the Neural Computation of Head Direction. Annual Review of Neuroscience. 43: 31-54
Gaffin, D. D., & Curry, C. M. (2020). Arachnid navigation–a review of classic and emerging models. The Journal of Arachnology, 48(1), 1-25.
Ronacher, B. Path integration in a three-dimensional world: the case of desert ants. J Comp Physiol A 206, 379–387 (2020).
When the wind blows
In the last few years, research on the Central Complex compass circuit in insects has been transformative in our ability to think about the underpinning mechanisms of the many remarkable navigation behaviours we have all studied. One thing that behavioural experiments have always shown is the ability of insects to utilise a broad array of sensory cues and to integrate or select between them based on reliability. Here, Okubo et al. show the the Ellipsoid Body compass circuit can be driven by wind information in the same way that it is driven by visual and self-motion cues. The wind information comes via ring neurons, similarly to visual information. Recently, it was shown elsewhere that plasticity in that visual input, can help tune the compass circuit to reliable local visual cues. Presumably a similar process happens to establish when wind information is reliable.
Okubo, T. S., Patella, P., D’Alessandro, I., & Wilson, R. I. (2020). A Neural Network for Wind-Guided Compass Navigation. Neuron.
The past is a foreign country; they do things differently there
I can’t get enough of articles like this. A historical account of the foundational work on invertebrate intelligence from pioneering scientists over the last 150 years. The value for me comes from the realisation of how intellectually brave one had to be to be able to see the interesting and important questions, never mind the possible answers. Here, Randolph Menzel has written a lovely piece. Accessible, informative and fascinating. More please!
Menzel, R. (2020). A short history of studies on intelligence and brain in honeybees. Apidologie, 1-12.
Which bit of a mini-brain for navigation?
One of the attractions of studying the mechanistic basis of behaviour in insects is the ability to accurately associate function to brain region, because insects show more repeatable behaviour and more identifiable and accessible neuropils. Of course even then, certain insect brain areas become well studied for particular tasks because that task is amenable in the lab, whereas others remain speculative. For instance, most of what we know about the role of the insect Mushroom Bodies come from olfactory learning, but it has remained speculation that these areas are involved in visual navigation. No longer. We now have two great papers that show how Mushroom Body deficits are clearly linked to learnt visual navigation without impacting on visual behaviour in general. Given what we have learnt about the functioning of Mushroom Bodies for appetitive and aversive conditioning, we can now build testable hypotheses about how these brain regions are involved in visual navigation.
Buehlmann, C., Wozniak, B., Goulard, R., Webb, B., Graham, P., & Niven, J. E. (2020). Mushroom Bodies Are Required for Learned Visual Navigation, but not for Innate Visual Behavior, in ants. Current Biology.
Kamhi, J. F., Barron, A. B., & Narendra, A. (2020). Vertical Lobes of the Mushroom Bodies Are Essential for View-Based Navigation in Australian Myrmecia Ants. Current Biology.
COVID catch-up: View-based navigation
Suffice to say 2020 has been a weird year and unfortunately the Insect Navigation BLOG has suffered in the covid turmoil. So here are some themed catch-up posts and normal service will resume for articles released from Aug 2020 onwards.
View-based navigation papers
Murray, T., Kocsi, Z., Dahmen, H., Narendra, A., Le Möel, F., Wystrach, A., & Zeil, J. (2020). The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi). Journal of Experimental Biology, 223(3).
Islam, M., Freas, C. A., & Cheng, K. (2020). Effect of large visual changes on the navigation of the nocturnal bull ant, Myrmecia midas. Animal Cognition, 1-10.
Wystrach, Antoine, Buehlmann, Cornelia, Schwarz, Sebastian, Cheng, Ken and Graham, Paul (2020) Rapid aversive and memory trace learning during route navigation in desert ants. Current Biology
Deeti, S., Fujii, K. & Cheng, K. The effect of spatially restricted experience on extrapolating learned views in desert ants, Melophorus bagoti. Anim Cogn (2020).
COVID catch-up: Computational Neuroscience
Suffice to say 2020 has been a weird year and unfortunately the Insect Navigation BLOG has suffered in the covid turmoil. So here are some themed catch-up posts and normal service will resume for articles released from Aug 2020 onwards.
Comp NS papers
Sun, X., Yue, S., & Mangan, M. (2020). A Decentralised Neural Model Explaining Optimal Integration of Navigational Strategies in Insects. eLife 2020;9:e54026
Pisokas, I., Heinze, S., & Webb, B. (2020). The head direction circuit of two insect species. Elife, 9, e53985.
Le Möel, F., & Wystrach, A. (2020). Opponent processes in visual memories: A model of attraction and repulsion in navigating insects’ mushroom bodies. PLoS computational biology, 16(2), e1007631.
COVID catch-up: Multimodal behaviour
Suffice to say 2020 has been a weird year and unfortunately the Insect Navigation BLOG has suffered in the covid turmoil. So here are some themed catch-up posts and normal service will resume for articles released from Aug 2020 onwards.
Multimodal navigation behaviour
Buehlmann, C., Aussel, A., & Graham, P. (2020). Dynamic multimodal interactions in navigating wood ants: What do path details tell us about cue integration?. Journal of Experimental Biology, 223(7).
De Agrò, M., Oberhauser, F.B., Loconsole, M. et al. Multi-modal cue integration in the black garden ant. Anim Cogn (2020).
Sasaki, T., Danczak, L., Thompson, B., Morshed, T., & Pratt, S. C. (2020). Route learning during tandem running in the rock ant Temnothorax albipennis. Journal of Experimental Biology, 223(9).
Schwarz, S., Clement, L., Gkanias, E. & Wystrach, A. 2020 How do backward-walking ants (Cataglyphis velox) cope with navigational uncertainty? Animal Behaviour 164, 133-142. doi: 10.1016/j.anbehav.2020.04.006.
Vermehren, J. A. V., Buehlmann, C., Fernandes, A. S., & Graham, P. (2020). Multimodal Influences on Learning Walks in Desert Ants (Cataglyphis fortis). J Comp Physiol A
COVID catch-up: Biorobotics
Suffice to say 2020 has been a weird year and unfortunately the Insect Navigation BLOG has suffered in the covid turmoil. So here are some themed catch-up posts and normal service will resume for articles released from Aug 2020 onwards.
Biorobotics papers
Webb, B. (2020). Robots with insect brains. Science, 368(6488), 244-245.
Differt, D., & Stürzl, W. (2020). A generalized multi-snapshot model for 3D homing and route following. Adaptive Behavior, 1059712320911217.
Dewar, A., Graham, P., Nowotny, T., & Philippides, A. (2020, July). Exploring the robustness of insect-inspired visual navigation for flying robots. In Artificial Life Conference Proceedings (pp. 668-677). MIT Press.
Dupeyroux, J., Lapalus, S., Brodoline, I., Viollet, S., & Serres, J. (2020, June). Insect-inspired omnidirectional vision for autonomous localization on-board a hexapod robot. In 28th Mediterranean Conference on Control and Automation (MED), 2020 IEEE International Conference on.
Dupeyroux, J., Viollet, S., & Serres, J. (2020, June). Bio-inspired celestial compass yields new opportunities for urban localization. In 28th Mediterranean Conference on Control and Automation (MED), 2020 IEEE International Conference on.
Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2020). A hybrid compact neural architecture for visual place recognition. IEEE Robotics and Automation Letters, 5(2), 993-1000.
Special types of learning
Across animals, many learning processes seem to follow similar characteristics, such as temporal discounting, where the time elapsed since an event changes the strength of the memory update. Here, Lionetti and Cheng investigate whether an important navigational process also follows a similar process. By offsetting outward from return journeys on an ants foraging trip, you can induce a recalibration of Path Integration mechanisms. By varying the delay between these two legs of the journey, one might expect different degrees of re-calibration, but in fact no such difference is observed. This suggests that not all learning and updating processes follow the same general principles.
Abstract: “Desert ants navigate by using two chief strategies: path integration, keeping track of the straight‐line distance and direction to the starting point as they travel, and landmark guidance, orientation based on the visual panorama. Both Cataglyphis ants in North Africa and Melophorus bagoti in Central Australia are known to adjust their vectors derived from path integration to compensate for mismatches between their outbound direction of travel and (the reverse of) the inbound direction of travel that takes them home, a process known as vector calibration. We created mismatches of 90° between the outbound vector and the homebound direction by displacing ants from a feeder before their homebound run. We examined temporal factors in vector calibration by varying the delay (0, 1 or 3 hr) between the outbound run to the feeder and the homebound run from the displacement site. According to the temporal weighting rule, such a delay should decrease the weight given to the vector information obtained from the outbound run. This in turn should favour reliance on the visual panorama and thus speed up calibration. Results did not support this prediction. At the displacement site, a delay had little effect on the extent of calibration or the speed of calibration (the number of trials to reach maximum calibration). Just before being displaced, ants were also tested in a test ring surrounded by high walls that obliterated the visual scenery. In the test ring, a delay made the ants less likely to rely on their vector: ants were often not oriented as a group. Otherwise, the ants in the test ring also did not calibrate any more or any faster.”
Lionetti, V. A., & Cheng, K. Vector calibration in Australian desert ants, Melophorus bagoti: Effects of a delay after the acquisition of vector information. Ethology.