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A neural network model for familiarity and context learning during honeybee foraging flights

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Abstract

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

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References

  • Ardin P, Peng F, Mangan M, Lagogiannis K, Webb B (2016) Using an insect mushroom body circuit to encode route memory in complex natural environments. PLoS Comput Biol 12(2):e1004,683

  • Asahina K, Louis M, Piccinotti S, Vosshall LB (2009) A circuit supporting concentration-invariant odor perception in drosophila. J Biol 8(1):9

    Article  PubMed  PubMed Central  Google Scholar 

  • Aso Y, Sitaraman D, Ichinose T, Kaun KR, Vogt K, Belliart-Guérin G, Plaçais PY, Robie AA, Yamagata N, Schnaitmann C et al (2014) Mushroom body output neurons encode valence and guide memory-based action selection in drosophila. Elife 3(e04):580

    Google Scholar 

  • Avarguès-Weber A, Giurfa M (2013) Conceptual learning by miniature brains. Proc R Soc Lond B Biol Sci 280(1772):20131,907

  • Baddeley B, Graham P, Husbands P, Philippides A (2012) A model of ant route navigation driven by scene familiarity. PLoS Comput Biol 8(1):e1002,336. doi:10.1371/journal.pcbi.1002336

  • Bazhenov M, Huerta R, Smith BH (2013) A computational framework for understanding decision making through integration of basic learning rules. J Neurosci 33(13):5686–5697

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Capaldi EA, Smith AD, Osborne JL, Fahrbach SE et al (2000) Ontogeny of orientation flight in the honeybee revealed by harmonic radar. Nature 403(6769):537

    Article  CAS  PubMed  Google Scholar 

  • Caron SJ, Ruta V, Abbott L, Axel R (2013) Random convergence of olfactory inputs in the drosophila mushroom body. Nature 497(7447):113–117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cassenaer S, Laurent G (2012) Corrigendum: conditional modulation of spike-timing-dependent plasticity for olfactory learning. Nature 487(7405):128–128. doi:10.1038/nature11261

    Article  CAS  Google Scholar 

  • Cheeseman JF, Millar CD, Greggers U, Lehmann K, Pawley MDM, Gallistel CR, Warman GR, Menzel R (2014) Way-finding in displaced clock-shifted bees proves bees use a cognitive map. Proc Natl Acad Sci 111(24):8949–8954. doi:10.1073/pnas.1408039111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cheung A, Collett M, Collett TS, Dewar A, Dyer F, Graham P, Mangan M, Narendra A, Philippides A, Stürzl W, Webb B, Wystrach A, Zeil J (2014) Still no convincing evidence for cognitive map use by honeybees: Fig. 1. Proc Natl Acad Sci 111(42):E4396–E4397. doi:10.1073/pnas.1413581111

  • Collett TS, Collett M (2002) Memory use in insect visual navigation. Nat Rev Neurosci 3(7):542–552. doi:10.1038/nrn872

    Article  CAS  PubMed  Google Scholar 

  • Cruse H, Wehner R (2011) No need for a cognitive map: decentralized memory for insect navigation. PLoS Comput Biol 7(3):e1002,009. doi:10.1371/journal.pcbi.1002009

  • Devaud JM, Papouin T, Carcaud J, Sandoz JC, Grünewald B, Giurfa M (2015) Neural substrate for higher-order learning in an insect: mushroom bodies are necessary for configural discriminations. Proc Natl Acad Sci 112(43):E5854–E5862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dylla KV, Raiser G, Galizia CG, Szyszka P (2017) Trace conditioning in drosophila induces associative plasticity in mushroom body kenyon cells and dopaminergic neurons. Front Neural Circuits 11:42

    Article  PubMed  PubMed Central  Google Scholar 

  • Farkhooi F, Froese A, Muller E, Menzel R, Nawrot MP (2013) Cellular adaptation facilitates sparse and reliable coding in sensory pathways. PLoS Comput Biol 9(10):e1003,251

  • Fernandez PC, Locatelli FF, Person-Rennell N, Deleo G, Smith BH (2009) Associative conditioning tunes transient dynamics of early olfactory processing. J Neurosci 29(33):10,191–10,202

  • Filla I, Menzel R (2015) Mushroom body extrinsic neurons in the honeybee (apis mellifera) brain integrate context and cue values upon attentional stimulus selection. J Neurophysiol 114(3):2005–2014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gupta N, Stopfer M (2012) Functional analysis of a higher olfactory center, the lateral horn. J Neurosci 32(24):8138–8148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haehnel M, Menzel R (2010) Sensory representation and learning-related plasticity in mushroom body extrinsic feedback neurons of the protocerebral tract. Front Syst Neurosci 4:161

    Article  PubMed  PubMed Central  Google Scholar 

  • Haenicke J (2015) Modeling insect inspired mechanisms of neural and behavioral plasticity. PhD thesis, Freie Universität Berlin

  • Hausler C, Nawrot MP, Schmuker M (2011) A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee. In: 5th International IEEE/EMBS conference on neural engineering (NER), pp 198–202

  • Heisenberg M (2003) Mushroom body memoir: from maps to models. Nat Rev Neurosci 4(4):266–275. doi:10.1038/nrn1074

    Article  CAS  PubMed  Google Scholar 

  • Helgadottir LI, Haenicke J, Landgraf T, Rojas R, Nawrot MP (2013) Conditioned behavior in a robot controlled by a spiking neural network. In: 6th International IEEE/EMBS conference on neural engineering (NER), 2013, pp 891–894

  • Hige T, Aso Y, Rubin GM, Turner GC (2015) Plasticity-driven individualization of olfactory coding in mushroom body output neurons. Nature 526(7572):258–262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the drosophila mushroom body. J Neurosci 31(33):11,772–11,785

  • Hope ACA (1968) A simplified Monte Carlo significance test procedure. J Roy Stat Soc B 30(3):582–98. doi:10.2307/2984263

    Google Scholar 

  • Huerta R, Nowotny T (2009) Fast and robust learning by reinforcement signals: explorations in the insect brain. Neural Comput 21(8):2123–2151

    Article  PubMed  Google Scholar 

  • Huerta R, Nowotny T, García-Sanchez M, Abarbanel HD, Rabinovich MI (2004) Learning classification in the olfactory system of insects. Neural Comput 16(8):1601–1640

    Article  PubMed  Google Scholar 

  • Hurley N, Rickard S (2009) Comparing measures of sparsity. IEEE Trans Inf Theory 55(10):4723–4741

    Article  Google Scholar 

  • Ito I, Ong RCy, Raman B, Stopfer M, (2008) Sparse odor representation and olfactory learning. Nat Neurosci 11(10):1177–1184

  • Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17(10):2443–2452. doi:10.1093/cercor/bhl152

    Article  PubMed  Google Scholar 

  • Jacobs LF, Menzel R (2014) Navigation outside of the box: what the lab can learn from the field and what the field can learn from the lab. Mov Ecol 2(1):3

    Article  PubMed  PubMed Central  Google Scholar 

  • Jortner RA, Farivar SS, Laurent G (2007) A simple connectivity scheme for sparse coding in an olfactory system. J Neurosci 27(7):1659–1669

    Article  CAS  PubMed  Google Scholar 

  • Kee T, Sanda P, Gupta N, Stopfer M, Bazhenov M (2015) Feed-forward versus feedback inhibition in a basic olfactory circuit. PLoS Comput Biol 11(10):e1004,531

  • Kloppenburg P, Nawrot MP (2014) Neural coding: sparse but on time. Curr Biol 24(19):R957–R959

    Article  CAS  PubMed  Google Scholar 

  • Laughlin SB, Horridge GA (1971) Angular sensitivity of the retinula cells of dark-adapted worker bee. Zeitschrift für Vergleichende Physiologie 74(3):329–335. doi:10.1007/BF00297733

    Article  Google Scholar 

  • Lengler J, Jug F, Steger A (2013) Reliable neuronal systems: the importance of heterogeneity. PLoS ONE 8(12):e80,694

  • Menzel R (2012) The honeybee as a model for understanding the basis of cognition. Nat Rev Neurosci 13(11):758–768

    Article  CAS  PubMed  Google Scholar 

  • Menzel R (2014) The insect mushroom body, an experience-dependent recoding device. J Physiol Paris 108(2):84–95

    Article  PubMed  Google Scholar 

  • Menzel R, Giurfa M (2001) Cognitive architecture of a mini-brain: the honeybee. Trends Cogn Sci 5(2):62–71. doi:10.1016/S1364-6613(00)01601-6

    Article  CAS  PubMed  Google Scholar 

  • Menzel R, Greggers U (2015) The memory structure of navigation in honeybees. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 201(6):547–61. doi:10.1007/s00359-015-0987-6

    Article  PubMed  Google Scholar 

  • Menzel R, Manz G (2005) Neural plasticity of mushroom body-extrinsic neurons in the honeybee brain. J Exp Biol 208(Pt 22):4317–32. doi:10.1242/jeb.01908

    Article  PubMed  Google Scholar 

  • Menzel R, Kirbach A, Haass WD, Fischer B, Fuchs J, Koblofsky M, Lehmann K, Reiter L, Meyer H, Nguyen H, Jones S, Norton P, Greggers U (2011) A common frame of reference for learned and communicated vectors in honeybee navigation. Curr Biol 21(8):645–650. doi:10.1016/j.cub.2011.02.039

    Article  CAS  PubMed  Google Scholar 

  • Montero A, Huerta R, Rodriguez FB (2015) Regulation of specialists and generalists by neural variability improves pattern recognition performance. Neurocomputing 151:69–77

    Article  Google Scholar 

  • Morrison A, Diesmann M, Gerstner W (2008) Phenomenological models of synaptic plasticity based on spike timing. Biol Cybern 98(6):459–478

    Article  PubMed  PubMed Central  Google Scholar 

  • Nadim F, Bucher D (2014) Neuromodulation of neurons and synapses. Curr Opin Neurobiol 29:48–56

    Article  CAS  PubMed  Google Scholar 

  • Nawrot MP (2012) Dynamics of sensory processing in the dual olfactory pathway of the honeybee. Apidologie 43(3):269–291

    Article  CAS  Google Scholar 

  • Nowotny T, Huerta R (2012) On the equivalence of Hebbian learning and the SVM formalism. In: 46th annual conference on information sciences and systems (CISS), 2012, pp 1–4

  • Nowotny T, Huerta R, Abarbanel HD, Rabinovich MI (2005) Self-organization in the olfactory system: one shot odor recognition in insects. Biol Cybern 93(6):436–446

    Article  PubMed  Google Scholar 

  • Olsen SR, Wilson RI (2008) Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452(7190):956–960

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Perez-Orive J, Mazor O, Turner GC, Cassenaer S, Wilson RI, Laurent G (2002) Oscillations and sparsening of odor representations in the mushroom body. Science 297(5580):359–365

    Article  CAS  PubMed  Google Scholar 

  • Riley JR, Greggers U, Smith AD, Reynolds DR, Menzel R (2005) The flight paths of honeybees recruited by the waggle dance. Nature 435(7039):205

    Article  CAS  PubMed  Google Scholar 

  • Schmuker M, Yamagata N, Nawrot M, M R, (2011) Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee. Front Neuroeng 4:17

  • Schmuker M, Pfeil T, Nawrot MP (2014) A neuromorphic network for generic multivariate data classification. Proc Natl Acad Sci 111(6):2081–2086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schwaerzel M, Monastirioti M, Scholz H, Friggi-Grelin F, Birman S, Heisenberg M (2003) Dopamine and octopamine differentiate between aversive and appetitive olfactory memories in drosophila. J Neurosci 23(33):10,495–10,502

  • Seelig JD, Jayaraman V (2015) Neural dynamics for landmark orientation and angular path integration. Nature 521(7551):186–191. doi:10.1038/nature14446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Serrano E, Nowotny T, Levi R, Smith BH, Huerta R (2013) Gain control network conditions in early sensory coding. PLoS Comput Biol 9(7):e1003,133

  • Smith BH, Huerta R, Bazhenov M, Sinakevitch I (2012) Distributed plasticity for olfactory learning and memory in the honey bee brain. In: Honeybee neurobiology and behavior, Springer, pp 393–408

  • Srinivasan MV (2014) Going with the flow: a brief history of the study of the honeybee’s navigational odometer’. J Comp Physiol A 200(6):563–573. doi:10.1007/s00359-014-0902-6

    Article  Google Scholar 

  • Strube-Bloss MF, Nawrot MP, Menzel R (2011) Mushroom body output neurons encode odor-reward associations. J Neurosci 31(8):3129–3140

    Article  CAS  PubMed  Google Scholar 

  • Strube-Bloss MF, Nawrot MP, Menzel R (2016) Neural correlates of side-specific odour memory in mushroom body output neurons. Proc R Soc B 283(1844):20161,270

  • Stürzl W, Zeil J (2007) Depth, contrast and view-based homing in outdoor scenes. Biol Cybern 96(5):519–531

    Article  PubMed  Google Scholar 

  • Stürzl W, Böddeker N, Dittmar L, Egelhaaf M (2010) Mimicking honeybee eyes with a 280 field of view catadioptric imaging system. Bioinspir Biomim 5(3):036,002

  • Szyszka P, Ditzen M, Galkin A, Galizia CG, Menzel R (2005) Sparsening and temporal sharpening of olfactory representations in the honeybee mushroom bodies. J Neurophysiol 94(5):3303–3313

    Article  PubMed  Google Scholar 

  • Szyszka P, Galkin A, Menzel R (2008) Associative and non-associative plasticity in Kenyon cells of the honeybee mushroom body. Front Syst Neurosci 2:3. doi:10.3389/neuro.06.003.2008

    Article  PubMed  PubMed Central  Google Scholar 

  • Tolman EC (1948) Cognitive maps in rats and men. Psychol Rev 55(4):189

    Article  CAS  PubMed  Google Scholar 

  • Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by. J Neurophysiol, pp 734–746. doi:10.1152/jn.01283.2007

  • Vitay J, Dinkelbach HÜ, Hamker FH (2015) ANNarchy: a code generation approach to neural simulations on parallel hardware. Front Neuroinform 9:19. doi:10.3389/fninf.2015.00019

  • von Frisch K (1967) The dance language and orientation of bees. Harvard University Press, Harvard

    Google Scholar 

  • Wessnitzer J, Young JM, Armstrong JD, Webb B (2012) A model of non-elemental olfactory learning in drosophila. J Comput Neurosci 32(2):197–212

    Article  PubMed  Google Scholar 

  • Wilson RI, Laurent G (2005) Role of gabaergic inhibition in shaping odor-evoked spatiotemporal patterns in the drosophila antennal lobe. J Neurosci 25(40):9069–9079

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Barbara Webb for fruitful discussions and Julian Petrasch and Sebastian Krieger for creating the drone map. This work was partially funded by the Bundesministerium für Bildung und Forschung (BMBF) through Grant No. 01GQ0941 to the Bernstein Focus Learning and Memory Berlin (Insect Inspired Robots: Towards an Understanding of Memory in Decision Making) and the Dr. Klaus Tschira Stiftung through Grant No. 00.300.2016 (Robotik in der Biologie: Ziele finden mit einem winzigen Gehirn. Die neuronalen Grundlagen der Navigation der Bienen).

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Authors and Affiliations

  1. Institute for Computer Science, Free University Berlin, Berlin, Germany

    Jurek Müller & Tim Landgraf

  2. Computational Systems Neuroscience, Institute for Zoology, University of Cologne, Cologne, Germany

    Martin Nawrot

  3. Institute for Neurobiology, Free University Berlin, Berlin, Germany

    Randolf Menzel

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  1. Jurek Müller
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  2. Martin Nawrot
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Correspondence to Tim Landgraf.

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This article belongs to a Special Issue on Neural Coding.

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Müller, J., Nawrot, M., Menzel, R. et al. A neural network model for familiarity and context learning during honeybee foraging flights. Biol Cybern 112, 113–126 (2018). https://doi.org/10.1007/s00422-017-0732-z

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