The “Brains on Board” team supports open-access publishing and aims to share and disseminate outcomes to researchers and the public alike. BoB publications will be populated further as the project matures. See the publications thus far which have been attributed to BoB:

“Brains on Board” Publications

de Croon, G.C.H.E., Dupeyroux, J.J.G., Fuller, S.B., Marshall, J.A.R. (2022) Insect-inspired AI for autonomous robots. Science Robotics, 7 (67) doi/10.1126/scirobotics.abl6334

Guiraud, M. , Roper, M., Wolf, S., Woodgate, J.L. & Chittka, L. (2022) Discrimination of edge orientation by bumblebees. PLoS one 17(6): e0263198 doi.org/10.1371/journal.pone.0263198

Kemppainen et al. (2022) Binocular mirror–symmetric microsaccadic sampling enables Drosophila hyperacute 3D vision. PNAS 119 (12) e2109717119 doi.org/10.1073/pnas.2109717119

Kemppainen, J., Mansour, N., Takalo, J. & Juusola, M. (2022) High-speed imaging of light-induced photoreceptor microsaccades in compound eyes. Communications Biology 5, 203 doi.org/10.1038/s42003-022-03142-0

Marshall, J. A. R. (2021) Borrowing bee brains. New Scientist 249(3322), 23 doi.org/10.1016/S0262-4079(21)00279-7

Brebner, J., Makinson, J., Bates, O., Rossi, N., Lim, K., Pasquaretta, C. Dubois, T.,Gomez-Moracho, T., Lihoreau, M., Chittka, L. & Woodgate, J.L. (2021) Bumblebees strategically use ground-level linear features in navigation. Animal Behaviour, 179: 147-160 doi.org/10.1016/j.anbehav.2021.07.003

J.L. Woodgate, J.C.Makinson, N.Rossi, K.S.Lim, A.M.Reynolds, C.J.Rawlings & L.Chittka (2021) ‘Harmonic radar tracking reveals that honeybee drones navigate between multiple aerial leks’ iScience https://doi.org/10.1016/j.isci.2021.102499

Gallo, V., Chittka, L. (2021) Stigmergy versus behavioral flexibility and planning in honeybee comb construction. PNAS 118 (33) e2111310118 doi.org/10.1073/pnas.2111310118

MaBouDi, H., Barron, A.B. , Li, S., Honkanen, M., Loukola, O.J., Peng, F., Li, W., Marshall, J.A.R., Cope, A., Vasilaki, E. & and Solvi, C. (2021) Non-numerical strategies used by bees to solve numerical cognition tasks Proceedings of the Royal Society Series B 288: 20202711 doi.org/10.1098/rspb.2020.2711

Brebner, J.S. & Chittka, L. (2021) Animal cognition: the self-image of a bumblebee. Current Biology, 31(4): R207-R209. doi.org/10.1016/j.cub.2020.12.027

Nityananda, V. & Chittka, L. (2021) Different effects of reward value and saliency during bumblebee visual research for multiple rewarding targets Animal Cognition  doi.org/10.1007/s10071-021-01479-3

MaBouDi, H., Marshall, J.A.R., Barron, A.B. (2020) Honeybees solve a multi-comparison ranking task by probability matching. Proceedings of the Royal Society Series B. doi:10.1098/rspb.2020.1525

MaBouDi, H., Galpayage, Dona, H.S., Gatto, E., Loukola, O.J., Buckley, E., Onoufriou, P.D., Skorupski, P., Chittka, L. (2020) Bumblebees use sequential scanning of countable items in visual patterns to solve numerosity tasks. Integrative and Comparative Biology 60: 929-942: https://doi.org/10.1093/icb/icaa025

MaBouDi, H., Solvi, C., Chittka, L. (2020) Bumblebees learn a relational rule but switch to a win-stay/lose-switch heuristic after extensive training. Frontiers in Behavioral Neuroscience 14(137): DOI: 10.3389/fnbeh.2020.00137

Solvi C., Gutierrez Al-Khudhairy S., & Chittka L. (2020) Bumble bees display cross-modal object recognition between visual and tactile senses. Science, 367, 6480 doi:10.1126/science.aay8064

Steinbeck, F., Adden, A., Graham, P. (2020) Connecting brain to behaviour: A role for general purpose steering circuits in insect orientation? Journal of experimental biology 223(4), doi.org/10.1242/jeb.212332

Stimberg, M., Goodman, D., & Nowotny, T. (2020). Brian2GeNN: accelerating spiking neural network simulations with graphics hardware. Scientific Reports, 10, 410. doi:10.1038/s41598-019-54957-7

Li, X., Abou Tayoun, A., Song, Z., Dau, A., Rien, D., Jaciuch, D., Dongre, S., Blanchard, F., Nikolaev, A., Zheng, L., Bollepalli, M.K., Chu, B., Hardie, R.C., Dolph, P.J., & Juusola, M. (2019). Ca2+-activated K+ channels reduce network excitability, improving adaptability and energetics for transmitting and perceiving sensory information. Journal of Neuroscience, 39 (36), 7132-7154. doi:10.1523/JNEUROSCI.3213-18.2019

Perry, C., & Chittka, L. (2019). How foresight might support the behavioural flexibility of arthropods. Current Opinion in Neurobiology, 54, 171-177. doi:10.1016/j.conb.2018.10.014

Vasas, V., Peng, F., MaBouDi, H., & Chittka, L. (2019) Randomly weighted receptor inputs can explain the large diversity of colour-coding neurons in the bee visual system. Scientific Reports, 9:8330. doi:10.1038/s41598-019-44375-0

Makinson, J.C., Woodgate, J.L., Reynolds, A., Capaldi, E.A., Perry, C.J., & Chittka, L. (2019) Harmonic radar tracking reveals random dispersal pattern of bumblebee (Bombus terrestris) queens after hibernation. Scientific Reports, 9:4651. doi:10.1038/s41598-019-40355-6

Vasas, V., & Chittka, L. (2019) Insect-inspired sequential inspection strategy enables an artificial network of four neurons to estimate numerosity. iScience 11, 85-92. doi: 10.1016/j.isci.2018.12.009

Cope A.J., Vasilaki E., Minors D., Sabo C., Marshall, J.A.R., & Barron, A.B. (2018) Abstract concept learning in a simple neural network inspired by the insect brain. PLoS Comput Biol 14(9). doi: 10.1371/journal.pcbi.1006435.

Knight J.C., & Nowotny T. (2018) GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model. Frontiers in Neuroscience 12:941. doi: 10.3389/fnins.2018.00941

Guiraud M., Roper M., & Chittka, L. (2018) High-speed videography reveals how honeybees can turn a spatial concept learning task into a simple discrimination task by stereotyped flight movements. Frontiers in Psychology 9, 900. doi: 10.3389/fpsyg.2018.01347

Gallo, V. & Chittka, L. (2018) Cognitive aspects of comb-building in the honeybee? Frontiers in Psychology 9, 1347. doi: 10.3389/fpsyg.2018.00900

Woodgate, J.L., Makinson, J.C., Lim, K.S., Reynolds A.M., & Chittka, L. (2017) Continuous radar tracking illustrates the development of multi-destination routes of bumblebees. Scientific Reports 7, 17323. doi:10.1038/s41598-017-17553-1

Sabo, C., Chisholm, R., Petterson, A., & Cope, A. (2017). A lightweight, inexpensive robotic system for insect vision. Arthropod Structure & Development doi.org/10.1016/j.asd.2017.08.001

Cope, A., Sabo, C., Vasilaki, E., Barron, A. B., & Marshall, J. A. R. (2017). A computational model of the integration of landmarks and motion in the insect central complex,” PLoS one doi: 10.1371/journal.pone.0172325

Sabo, C., Yavuz, E., Cope, A., Gurney, K., Vasilaki, E., Nowotny, T., & Marshall, J. A. R. (2017). An inexpensive flying robot design for embodied robotics research. 2017 International Joint Conference on Neural Networks. Anchorage, Alaska, May 14-19.

Related Publications

Wystrach, A., Schwarz, S., Graham, P., & Cheng, K.(2019). Running paths to nowhere: repetition of routes shows how navigating ants modulate online the weights according to cues. Animal Cognition 22(2), 213-222. doi:10.1007/s10071-019-01236-7

Johnson, C., Philippides, A., & Husbands, P. (2019). Simulating soft-bodied swimmers with particle-based physics. Soft Robotics, 6 (2), 263-275. doi:10.1089/soro.2018.0027

Peng, F., & Chittka, L. (2017). A simple computational model of the bee mushroom body can explain seemingly complex forms of olfactory learning and memory. Current Biology 27(2), 224-230. doi: 10.1016/j.cub.2016.10.054.

Roper, M., Fernando, C., & Chittka, L. (2017). Insect bio-inspired neural network provides new evidence on how simple feature detectors can enable complex visual generalization and stimulus location invariance in the miniature brain of honeybees. PLoS Computational Biology 13(2): e1005333. doi:10.1371/journal.pcbi.1005333.

Cope, A., Sabo, C., Gurney, K., Vasilaki, E., & Marshall, J. A. R. (2016). A model for an angular velocity-tuned motion detector accounting for deviations in the corridor-centring response of the bee. PLoS Computational Biology. doi: 10.1371/journal.pcbi.1004887.s001.

Yavuz, E., Turner, J., & Nowotny, T. (2016). GeNN: a code generation framework for accelerated brain simulations. Scientific Reports 6, 18854 doi: 10.1038/srep18854.

Sabo, C., Cope, A., Gurney, K., Vasilaki, E., & Marshall, J. A. R. (2016). Bio-inspired visual navigation for a quadcopter using optic flow. AIAA Infotech@Aerospace. San Diego, CA, Jan., AIAA 2016-0404.

Additionally, please review the published outcomes from the “Green Brain Project” here: https://www.sheffield.ac.uk/greenbrain