[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

A Computational Model for the Insect Brain

  • Chapter
  • First Online:
Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II

Part of the book series: Cognitive Systems Monographs ((COSMOS,volume 21))

Abstract

As seen in the Chap. 1, the fruit fly Drosophila melanogaster is an extremely interesting insect because it shows a wealth of complex behaviors, despite its small brain. Nowadays genetic techniques allow to knock out the function of defined parts or genes in the Drosophila brain. Together with specific mutants which show similar defects in those parts or genes, hypothesis about the functions of every single brain part can be drawn. Based upon the results reported in the Chap. 1, a computational model of the fly Drosophila has been designed and implemented to emulate the functionalities of the two relevant centres present in insects: the Mushroom Bodies and the Central Complex. Their actions and inter-actions are adapted from the neurobiological prospective to a computational implementation. A complete block scheme is proposed where the proved or conjectured interactions among the identified blocks are depicted. Several simulations results are finally provided to demonstrate the capability of the system both considering specific parts of the complete structure for comparison with insect experiments, and the whole model for more complex simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Webb, T.R. Consi, Biorobotics: Methods and Applications (AAAI Press/MIT Press, Menlo Park, 2001)

    Google Scholar 

  2. M. Dorigo, T. Stutzle, Ant Colony Optimization (MIT Press, Cambridge, 2004)

    Google Scholar 

  3. M.V. Srinivasan, S. Zhang, M. Altwein, J. Tautz, Honeybee navigation: nature and calibration of the odometer. Science 287(5454), 851–853 (2000)

    Article  Google Scholar 

  4. T.W. Secomb F.G. Barth, J.A.C. Humphrey, Locust’s looming detectors for robot sensors. in Sensors and sensing in biology and engineering, ed. by F. Rind, R. Santer, J. Blanchard, P. Verschure (springerwien, newyork, 2003)

    Google Scholar 

  5. B. Webb, T. Scutt, A simple latency dependent spiking neuron model of cricket phonotaxis. Biol. Cybern. 82(3), 247–269 (2000)

    Article  Google Scholar 

  6. P. Arena, L. Fortuna, M. Frasca, L. Patané, M. Pavone, Realization of a CNN-driven cockroach-inspired robot, in International Conference on Circuits and Systems (ISCAS), 2006

    Google Scholar 

  7. J.T. Watson, R.E. Ritzmann, S.N. Zill, A.J. Pollack, Control of obstacle climbing in the cockroach, Blaberus discoidalis. I. Kinematics. J Comp Physiol A 188, 39–53 (2002)

    Google Scholar 

  8. H. Cruse, T. Kindermann, M. Schumm, J. Dean, J. Schmitz, Walknet a biologically inspired network to control six-legged walking. Neural Networks 11, 1435–1447 (1998)

    Google Scholar 

  9. B. Webb, J. Wessnitzer, Multimodal sensory integration in insects—towards insect brain control architectures. Bioinspiration Biomimetics 1, 63 (2006)

    Article  Google Scholar 

  10. U. Homberg, Structure and functions of the central complex in insects. in Arthropod Brain, Its Evolution, Development, Structure and Functions, vol. 347–367, ed. by A.P Gupta, (Wiley, New York, 1987)

    Google Scholar 

  11. G.O. Lopez-Riquelme, W. Gronenberg, Multisensory convergence in the mushroom bodies of ants and bees. Acta. Biol. Hung. 55, 31–37 (2004)

    Article  Google Scholar 

  12. P. Arena, L. Fortuna, M. Frasca, L. Patané, Learning anticipation via spiking networks: application to navigation control. IEEE Trans. Neural Networks 20(2), 202–216 (2009)

    Article  Google Scholar 

  13. P. Arena, L. Patané, (eds.), Spatial temporal patterns for action-oriented perception in roving robots. in Cognitive Systems Monographs, vol. 1 (Springer, Berlin, 2009)

    Google Scholar 

  14. P.F.M.J. Verschure, T. Voegtlin, R.J. Douglas, Environmentally mediated synergy between perception and behaviour in mobile robots. Nature 425, 620–624 (2003)

    Article  Google Scholar 

  15. R. Brooks, New approaches to robotics. Science 253, 1227–1232 (1991)

    Article  Google Scholar 

  16. R.C. Arkin, Behavior-Based Robotics (MIT Press, Cambridge, 1998)

    Google Scholar 

  17. P. Arena, L. Patané, P.S. Termini, Decision making processes in the fruit fly: a computational model. in Frontiers in Artificial Intelligence and Applications—Proceedings of the 21st Italian Workshop on Neural Nets, vol. 234 (Seville, Spain, 2011), pp. 284–291

    Google Scholar 

  18. E. Arena, P. Arena, L. Patané, Efficient hexapodal locomotion control based on flow-invariant subspaces. in 18th World Congress of the International Federation of Automatic Control (IFAC), Milan, Italy, 2011

    Google Scholar 

  19. G.M. Shepherd, Neurobiology (Oxford University, New York, 1997)

    Google Scholar 

  20. P. Arena, C. Berg, L. Patané, R. Strauss, P.S. Termini, An insect brain computational model inspired by Drosophila melanogaster: architecture description, WCCI 2010 IEEE World Congress on Computational Intelligence (Barcelona, Spain, 2010), pp. 831–837

    Google Scholar 

  21. P. Arena, M. Cosentino, L. Patané, A. Vitanza, Sparkrs4cs: a software/hardware framework for cognitive architectures, 5th SPIE’s International Symposium on Microtechnologies (Czech Republic, Prague, 2011), pp. 1–12

    Google Scholar 

  22. P. Arena, L. Patané, Simple sensors provide inputs for cognitive robots. Instrum. Measur. Mag. 12(3), 13–20 (2009)

    Article  Google Scholar 

  23. E.M. Izhikevich, Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)

    Article  MathSciNet  Google Scholar 

  24. R. Strauss, M. Mronz, Visual motion integration controls attractiveness of objects in walking flies and a mobile robot, International Conference on Intelligent Robots and Systems (Nice, France, 2008), pp. 3559–3564

    Google Scholar 

  25. L. Alba, P. Arena, S. De Fiore, L. Patane, R. Strauss, G. Vagliasindi, Implementation of a Drosophila-inspired model on the eye-ris platform. in Proceedings of the IEEE International Conference of CNNA (Berkley, 2010)

    Google Scholar 

  26. G. Liu, H. Seiler, A. Wen, T. Zars, K. Ito, R. Wolf, M. Heisenberg, L. Liu, Distinct memory traces for two visual features in the Drosophila brain. Nature 439, 551–556 (2006)

    Article  Google Scholar 

  27. H. Hoffmann, R. Moller, An extension of neural gas to local pca. Neurocomputing 62, 305–326 (2004)

    Article  Google Scholar 

  28. P. Arena, S. De Fiore, L. Patané, P.S. Termini, R. Strauss, Visual learning in Drosophila: application on a roving robot and comparisons, 5th SPIE’s International Symposium on Microtechnologies (Czech Republic, Prague, 2011), pp. 1–12

    Google Scholar 

  29. K. Neuser, T. Triphan, M. Mronz, B. Poeck, R. Strauss, Analysis of a spatial orientation memory in Drosophila. Nature 453, 1244–1247 (2008)

    Article  Google Scholar 

  30. R. Wehner, G. Hartmann, The ant’s path integration system: a neural architecture. Biol. Cybern. 73, 483–497 (1995)

    MATH  Google Scholar 

  31. R.L. Davis, X. Liu, Insect olfactory memory in time and space. Curr. Opin. Neurobiol. 6, 679–685 (2006)

    Google Scholar 

  32. J. Dubnau, C. Margulies, T. Tully, Deconstructing memory in Drosophila. Curr. Biol. 15, 700 (2005)

    Google Scholar 

  33. L.F. Abbott, S. Song, K.D. Miller, Competitive hebbian learning through spike-timing-dependent plasticity. Nat. Neurosci. 3, 919–926 (2000)

    Google Scholar 

  34. L.F. Abbott, S. Song, Cortical development and remapping through spike timing-dependent plasticity. Neuron 32, 339–350 (2001)

    Article  Google Scholar 

  35. P. Arena, S. De Fiore, L. Patané, M. Pollino, C. Ventura, Stdp-based behavior learning on tribot robot. in Proceedings of IEEE/RSJ International Conference SPIE (2009)

    Google Scholar 

  36. L. Vine, L. Gottschalk, P.J. Shaw, L. Seugnet, Y. Suzuki, D1 receptor activation in the mushroom bodies rescues sleep-loss-induced learning impairments in Drosophila. Curr. Biol. 18, 1110–1117 (2008)

    Google Scholar 

  37. R. Biesinger, K.G. Gotz, Centrophobism in Drosophila melanogaster. Physiological approach to search and search control. J. Comp. Physiol. A 156, 329–337 (1987)

    Google Scholar 

  38. M.B. Sokolowski, S. De Belle, Heredity of rover/sitter: alternative foraging strategies of Drosophila melanogaster larvae. Heredity 5(9), 73–83 (1987)

    Google Scholar 

  39. J.R. Martin M. Besson, Centrophobism/thigmotaxis, a new role for the mushroom bodies in Drosophila. J. Neurobiol. 62, 386–394 (2005)

    Google Scholar 

  40. A. Wen, T. Zars, K. Ito, R. Wolf, M. Heisenberg, L. Liu, G. Liu, H. Seiler, Distinct memory traces for two visual features in the Drosophila brain. Nature 439, 551–556 (2006)

    Article  Google Scholar 

  41. P. Arena, L. Patané, P.S. Termini, A. Vitanza, R. Strauss, Software/hardware issues in modelling insect brain architecture. in 4th International Conference on Intelligent Robotics and Applications (ICIRA), Aachen (Germany), 2011

    Google Scholar 

  42. P. Arena, L. Patané, P.S. Termini, An insect brain computational model inspired by Drosophila melanogaster: simulation results, WCCI 2010 IEEE World Congress on Computational Intelligence (Barcelona, Spain, 2010), pp. 838–845

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical, Electronic and Computer Science Engineering, University of Catania, 95125, Catania, Italy

    P. Arena, L. Patanè & P. S. Termini

Authors
  1. P. Arena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Patanè
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. S. Termini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Arena .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Università di Catania, Catania, Italy

    Paolo Arena

  2. Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, Università di Catania, Catania, Italy

    Luca Patanè

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Arena, P., Patanè, L., Termini, P.S. (2014). A Computational Model for the Insect Brain. In: Arena, P., Patanè, L. (eds) Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II. Cognitive Systems Monographs, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-02362-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02362-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02361-8

  • Online ISBN: 978-3-319-02362-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation