Skip to main content
Springer Nature Link
Log in

Perception for Action in Insects

  • Chapter
Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots

Part of the book series: Cognitive Systems Monographs ((COSMOS,volume 1))

  • 1110 Accesses

Abstract

We review the concept of ‘perception for action’, contrasting the traditional view of perception as internal representation with the idea of transformation in a closed loop system. This introduces recent approaches using active perception, dynamical systems theory, action-based agent architectures and consideration of the role of predictive loops. We then apply these ideas to insect behaviour and neurophysiology, with particular attention to higher brain centres. We propose an insect brain control architecture for robotics.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abarbanel, H., Rabinovich, M.: Neurodynamics: nonlinear dynamics and neurobiology. Current Opinion in Neurobiology 11, 423–430 (2001)

    Article  Google Scholar 

  2. Arkin, R.: Integrating behavioral, perceptual, and world knowledge in reactive navigation. Robotics and Autonomous Systems 6, 105–122 (1990)

    Article  Google Scholar 

  3. Atkins, M.D.: Introduction to insect behavior. Macmillan Publishing Co., Inc., New York (1980)

    Google Scholar 

  4. Ballard, D.: Animate vision. Artificial Intelligence 48, 57–86 (1991)

    Article  Google Scholar 

  5. Bausenwein, B., Mueller, N., Heisenberg, M.: Behavior-dependent activity labeling in the central complex of Drosophila during controlled visual stimulation. Journal of Comparative Neurology 340, 255–268 (1994)

    Article  Google Scholar 

  6. Beer, R.: A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 173–215 (1995)

    Google Scholar 

  7. Beer, R.: Dynamical approaches to cognitive science. Trends in Cognitive Science 4(3), 91–99 (2000)

    Article  MathSciNet  Google Scholar 

  8. Beintema, J., van den Berg, A.: Heading detection using motion templates and eye velocity gain fields. Vision Research 38, 2155–2179 (1998)

    Article  Google Scholar 

  9. Bennett, A.: Do animals have cognitive maps? Journal of Experimental Biology 199, 219–224 (1996)

    Google Scholar 

  10. van den Berg, A., Beintema, J.: Motion templates with eye velocity gain fields for transformation of retinal to head centric flow. NeuroReport 8, 835–840 (1997)

    Google Scholar 

  11. Birmingham, J.: Increasing sensor flexibility through neuromodulation. Biological Bulletin 200, 206–210 (2001)

    Article  Google Scholar 

  12. Birmingham, J., Tauck, D.: Neuromodulation in invertebrate sensory systems: from biophysics to behavior. Journal of Experimental Biology 206, 3541–3546 (2003)

    Article  Google Scholar 

  13. Bisch-Knaden, S., Wehner, R.: Local vectors in desert ants: context-dependent landmark learning during outbound and homebound runs. Journal of Comparative Physiology 189, 181–187 (2003)

    Google Scholar 

  14. Brooks, R.: Intelligence without reason. In: Proceedings of IJCAI 1991 (1991)

    Google Scholar 

  15. Brooks, R.: Intelligence without representation. Artificial Intelligence 47, 139–159 (1991)

    Article  Google Scholar 

  16. Brooks, R.: From earwigs to humans. Robotics and Autonomous Systems 20(2-4), 291–304 (1997)

    Article  Google Scholar 

  17. Carpenter, G., Grossberg, S.: Adaptive Resonance Theory, pp. 87–90. MIT Press, Cambridge (2003)

    Google Scholar 

  18. Carpenter, G.A., Grossberg, S.: The art of adaptive pattern recognition by a self-organizing neural network. Computer 21(3), 77–88 (1988)

    Article  Google Scholar 

  19. Clark, A.: Embodiment: from fish to fantasy. Trends in Cognitive Sciences 3(9), 345–351 (1999)

    Article  Google Scholar 

  20. Clark, A., Grush, R.: Towards a cognitive robotics. Adaptive Behavior 7, 5–16 (1999)

    Article  Google Scholar 

  21. Clayton, K., Frey, B.: Inter- and intra-trial dynamics in memory and choice. In: Nonlinear Dynamics in Human Behavior. World Scientific, Singapore (1996)

    Google Scholar 

  22. Collett, T., Collett, M.: Path integration in insects. Current Opinion in Neurobiology 10, 757–762 (2000)

    Article  Google Scholar 

  23. Collins, S., Ruina, A., Tedrake, R., Wisse, M.: Efficient bipedal robots based on passive-dynamic walkers. Science 307, 1082–1085 (2005)

    Article  Google Scholar 

  24. Comer, C., Robertson, R.: Identified nerve cells and insect behavior. Progress in Neurobiology 63, 409–439 (2001)

    Article  Google Scholar 

  25. Conklin, J., Eliasmith, C.: A controlled attractor network model of path integration in the rat. Journal of Computational Neuroscience 18(2), 183–203 (2005)

    Article  MathSciNet  Google Scholar 

  26. Cos, I., Hayes, G.: Behaviour control using a functional and emotional model. In: Proceedings of the 7th Conference on the Simulation of Adaptive Behaviour. The MIT Press, Edinburgh (2002)

    Google Scholar 

  27. Cos-Aguilera, I., Hayes, G., Canamero, L.: Using a SOFM to learn object affordances. In: Proceedings of the 5th Workshop on Physical Agents (WAF 2004), Girona, Catalonia, Spain (2004)

    Google Scholar 

  28. Craik, K.: The nature of explanation. Cambridge University Press, Cambridge (1943)

    Google Scholar 

  29. Cruse, H.: The evolution of cognition - a hypothesis. Cognitive Science 27, 135–155 (2003)

    Article  Google Scholar 

  30. Deneubourg, J.L., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. Biol. Bull. 202(3), 262–267 (2002)

    Article  Google Scholar 

  31. Derby, C., Steullet, P.: Why do animals have so many receptors? The role of multiple chemosensors in animal perception. Biological Bulletin 200, 211–215 (2001)

    Article  Google Scholar 

  32. Dolcomyn, F.: Insect walking and robotics. Annual Review of Entomology 49, 51–70 (2004)

    Article  Google Scholar 

  33. Dubnau, J., Tully, T.: Gene discovery in Drosophila: new insights for learning and memory. Annual Review of Neuroscience 21, 407–444 (1998)

    Article  Google Scholar 

  34. Eliasmith, C.: Computation and dynamical models of mind. Minds and machines 7, 531–541 (1997)

    Article  Google Scholar 

  35. Eliasmith, C., Anderson, C.: Neural engineering - computation, representation, and dynamics in neurobiological systems. The MIT Press, Cambridge (2003)

    Google Scholar 

  36. Elman, J.: Finding structure in time. Cognitive Science 14(2), 179–211 (1990), http://www.isrl.uiuc.edu/~amag/langev/paper/elman90findingStructure.html

    Article  Google Scholar 

  37. Engel, A., Fries, P., Singer, W.: Dynamic predictions: oscillations and synchrony in top-down processing. Nature 2, 704–716 (2001)

    Google Scholar 

  38. Evans, H.E.: Wasp farm. Anchor Natural History Books. Anchor Press/ Doubleday and Company, New York (1973)

    Google Scholar 

  39. Fahrbach, S.: Structure of the mushroom bodies of the insect brain. Annual Review of Entomology 51, 209–232 (2006)

    Article  Google Scholar 

  40. Fahrbach, S., Giray, T., Farris, S., Robinson, G.: Expansion of the neuropil of the mushroom bodies in male honeybees is coincident with initiation of flight. Neuroscience Letters 236, 135–138 (1997)

    Article  Google Scholar 

  41. Farivar, S.: Cytoarchitecture of the locust olfactory system. Ph.D. thesis, California Institute of Technology (2005)

    Google Scholar 

  42. Freeman, W.: A neurobiological theory of meaning in perception. I. Information and meaning in nonconvergent and nonlocal brain dynamics. International Journal of Bifurcation and Chaos 13(9) (2003)

    Google Scholar 

  43. Gaver, W.: What in the world do we hear? An ecological approach to auditory source perception. Ecological Psychology 5, 1–29 (1993)

    Article  Google Scholar 

  44. Gerber, B., Tanimoto, H., Heisenberg, M.: An engram found? Evaluating the evidence from fruit flies. Current Opinion in Neurobiology 14, 737–744 (2004)

    Article  Google Scholar 

  45. Gibson, J.: The ecological approach to visual perception. Houghton Mifflin, Boston (1979)

    Google Scholar 

  46. Glazier, P., Davids, K., Bartlett, R.: Dynamical systems theory: a relevant framework for performance-oriented sports biomechanics research. Sportscience 7 (2003)

    Google Scholar 

  47. Gregory, R.: Perceptions as hypotheses. Philosophical Transactions of the Royal Society of London, B 290, 181–197 (1980)

    Article  Google Scholar 

  48. Gregson, R.: n-Dimensional non-linear psychophysics. Erlbaum, Mahwah (1992)

    Google Scholar 

  49. Gronenberg, W.: Subdivisions of hymenopteran mushroom body calyces by their afferent supply. Journal of Comparative Neurology 436, 474–489 (2001)

    Article  Google Scholar 

  50. Grossberg, S.: Neural networks and natural intelligence. MIT Press, Cambridge (1988)

    Google Scholar 

  51. Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behavioral and Brain Sciences (in press, 2003)

    Google Scholar 

  52. Grush, R.: In defense of some ‘cartesian’ assumptions concerning the brain and its operations. Biology and Philosophy 18, 53–93 (2003)

    Article  Google Scholar 

  53. Guastello, S.: Nonlinear dynamics in psychology. Discrete dynamics in nature and society 6, 11–29 (2001)

    Article  MATH  Google Scholar 

  54. Gurney, K., Humphries, M., Wood, R., Prescott, T., Redgrave, P.: Testing computational hypotheses of brain systems function: a case study with the basal ganglia. Network: Computation in Neural Systems 15, 263–290 (2004)

    Article  Google Scholar 

  55. Haken, H.: Synergetics: an introduction. Springer, Berlin (1983)

    MATH  Google Scholar 

  56. Hammer, M.: An identified neuron mediates the unconditioned stimulus in associative olfactory learning in honeybees. Nature 366, 59–63 (1993)

    Article  Google Scholar 

  57. Hammer, M.: The neural basis of associative reward learning in honeybees. Trends in Neuroscience 20(6), 245–252 (1997)

    Article  Google Scholar 

  58. Hammer, M., Menzel, R.: Multiple sites of associative odor learning as revealed by local brain microinjections of octopamine in honeybees. Learning and Memory 5, 146–156 (1998)

    Google Scholar 

  59. Hanesch, U., Fischbach, K.F., Heisenberg, M.: Neuronal architecture of the central complex in Drosophila melanogaster. Cell Tissue Research 257, 343–366 (1989)

    Article  Google Scholar 

  60. Harter, D., Kozma, R.: Navigation and cognitive map formation using aperiodic neurodynamics. In: From Animals to Animats 8: The Eighth International Conference on the Simulation of Adaptive Behavior (SAB 2004), pp. 450–455. MIT Press, Cambridge (2004)

    Google Scholar 

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

    MATH  Google Scholar 

  62. Heath, R.: Nonlinear dynamics: techniques and applications in psychology. Lawrence Erlbaum Associates, Mahwah (2000)

    Google Scholar 

  63. Heisenberg, M.: Central brain function in insects: genetic studies on the mushroom bodies and central complex in Drosophila. In: Neural Basis of Behavioural Adaptations. Fortschritte der Zoologie, vol. 39, pp. 61–79. Gustav Fischer Verlag, Stuttgart (1994)

    Google Scholar 

  64. Heisenberg, M.: Pattern recognition in insects. Current Opinion in Neurobiology 5, 475–481 (1995)

    Article  Google Scholar 

  65. Heisenberg, M.: What do the mushroom bodies do for the insect brain? an introduction. Learning and Memory 5, 1–10 (1998)

    Google Scholar 

  66. Heisenberg, M., Heusipp, M., Wanke, C.: Structural plasticity in the Drosophila brain. Journal of Neuroscience 15, 1951–1960 (1995)

    Google Scholar 

  67. Helfrich-Foerster, C.: Neurobiology of the fruit fly’s circadian clock. Genes, Brain and Behavior 4, 65–76 (2005)

    Article  Google Scholar 

  68. Hesslow, G.: Conscious thought as simulation of behaviour and perception. Trends in Cognitive Sciences 6(6), 242–247 (2002)

    Article  Google Scholar 

  69. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9, 2451–2471 (1997)

    Google Scholar 

  70. Homberg, U.: Structure and function of the central complex in insects. In: Arthropod brain: its evolution, development, structure and functions, pp. 347–367. Wiley, NY (1987)

    Google Scholar 

  71. Homberg, U.: The central complex in the brain of the locust: anatomical and physiological characterisation. In: Elsner, N., Roth, G. (eds.) Brain-Perception-Cognition. Thieme, Stuttgart (1990)

    Google Scholar 

  72. Homberg, U.: Flight-correlated activity changes in neurons of the lateral accessory lobes in the brain of the locust Schistocerca gregaria. Journal of Comparative Physiology A 175, 597–610 (1994)

    Article  Google Scholar 

  73. Homberg, U.: In the search of the sky compass in the insect brain. Naturwissenschaften 91, 199–208 (2004)

    Article  Google Scholar 

  74. Homberg, U.: Multisensory processing in the insect brain. In: Methods in Insect Sensory Neuroscience. CRC Press, Boca Raton (2005)

    Google Scholar 

  75. Homberg, U., Christensen, T., Hildebrand, J.: Structure and function of the deutocerebrum in insects. Annual Review of Entomology 34, 477–501 (1989)

    Article  Google Scholar 

  76. Homberg, U., Reischig, T., Stengl, M.: Neural organisation of the circadian system of the cockroach Leucophaea maderae. Chronobiol. Int. 20, 577–591 (2003)

    Article  Google Scholar 

  77. Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA 79, 2554–2558 (1982)

    Article  MathSciNet  Google Scholar 

  78. Hoshino, O.: Dynamic interaction of attractors across multiple cortical networks as a neural basis for intersensory facilitation. Connection Science 14, 345–375 (2002)

    Article  Google Scholar 

  79. Hoshino, O.: Coherent interaction of dynamical attractors for object-based selective attention. Biological Cybernetics 89, 107–118 (2003)

    Article  MATH  Google Scholar 

  80. Huerta, R., Nowotny, T., Garcia-Sanchez, M., Abarbanel, H., Rabinovich, M.: Learning classification in the olfactory system of insects. Neural Computation 16, 1601–1640 (2004)

    Article  MATH  Google Scholar 

  81. Hurley, S.: Perception and action: alternative views. Synthese 129, 3–40 (2001)

    Article  Google Scholar 

  82. Husbands, P., Harvey, I., Cliff, D., Miller, G.: The use of genetic algorithms for the development of sensorimotor control systems. In: Proceedings of the PerAc 1994 Conference. IEEE Computer Society Press, Los Alamitos (1994)

    Google Scholar 

  83. Ikeno, H., Usui, S.: Basic computational properties of Kenyon cell in the mushroom body of honeybee. Neurocomputing 32, 167–172 (2000)

    Article  Google Scholar 

  84. Ito, K., Suzuki, K., Estes, P., Ramaswami, M., Yamamoto, D., Strausfeld, N.: The organisation of extrinsic neurons and their implications in the functional roles of the mushroom bodies in Drosophila melanogaster meigen. Learning and Memory 5, 52–77 (1998)

    Google Scholar 

  85. Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Proc. NIPS 2002 (2002)

    Google Scholar 

  86. Keijzer, F.: Representation in dynamical and embodied cognition. Cognitive Systems Research 3, 275–288 (2002)

    Article  Google Scholar 

  87. Kelso, J.: Dynamic patterns: the self-organisation of brain and behaviour. MIT Press, Cambridge (1995)

    Google Scholar 

  88. Kersten, D., Yuille, A.: Bayesian models of object perception. Current opinions in neurobiology 13, 1–9 (2003)

    Article  Google Scholar 

  89. Kirsh, D.: Today the earwig, tomorrow man? Artificial Intelligence 47, 161–184 (1991)

    Article  MathSciNet  Google Scholar 

  90. Kirsh, D.: The intelligent use of space. Artificial Intelligence 73, 31–68 (1995)

    Article  Google Scholar 

  91. Korn, H., Faure, P.: Is there chaos in the brain? II. Experimental evidence and related models. Comptes Rendus Biologies 326, 787–840 (2003)

    Article  Google Scholar 

  92. Kosko, B.: Adaptive bidirectional associative memories. Applied Optics 26, 4947–4960 (1987)

    Article  Google Scholar 

  93. Kosslyn, S., Ganis, G., Thompson, W.: Neural foundations of imagery. Nature Reviews 2, 635–642 (2001)

    Article  Google Scholar 

  94. Labhart, T., Meyer, E.: Detectors for polarized skylight in insects: a survey of ommatidial specialisations in the dorsal rim area of the compound eye. Microscopy Research and Technique 47, 368–379 (1999)

    Article  Google Scholar 

  95. Lakoff, G., Johnson, M.: Metaphors We Live By. University of Chicago Press, Chicago (1980)

    Google Scholar 

  96. Lappe, M., Bremmer, F., van den Berg, A.: Perception of self-motion from visual flow. Trends in Cognitive Sciences 3(9), 329–336 (1999)

    Article  Google Scholar 

  97. Laurent, G.: Olfactory processing: maps, time and codes. Current Opinion in Neurobiology 7, 547–553 (1997)

    Article  Google Scholar 

  98. Laurent, G.: Olfactory network dynamics and the coding of multidimensional signals. Nature Reviews Neuroscience 3(11), 884–895 (2002)

    Article  Google Scholar 

  99. Lederman, S., Klatzky, R.: Haptic aspects of motor control. In: Jeannerod, M. (ed.) Handbook of Neuropsychology. Action and Cognition, vol. 11. Elsevier Science Publishers, Amsterdam (1996)

    Google Scholar 

  100. Lee, D.: A theory of visual control of braking based on information about time-to-collision. Perception 5, 437–459 (1976)

    Article  Google Scholar 

  101. Li, Y., Strausfeld, N.: Morphology and sensory modality of mushroom body extrinsic neurons in the brain of the cockroach. Journal of Comparative Neurology 387, 631–650 (1997)

    Article  Google Scholar 

  102. Li, Y., Strausfeld, N.: Multimodal efferent and recurrent neurons in the medial lobes of cockroach mushroom bodies. Journal of Comparative Neurology 409, 647–663 (1999)

    Article  Google Scholar 

  103. Li, Z., Dayan, P.: Computational differences between asymmetrical and symmetrical networks. Network 10, 59–77 (1999)

    Article  MATH  Google Scholar 

  104. Liberman, A., Cooper, F., Shankweller, D., Studdert, M.: Perception of the speech code. Psychological Review 74, 431–461 (1967)

    Article  Google Scholar 

  105. Liberman, A., Mattingly, I.: The motor theory of speech perception revised. Cognition 21, 1–36 (1985)

    Article  Google Scholar 

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

    Article  Google Scholar 

  107. Liu, L., Wolf, R., Ernst, R., Heisenberg, M.: Context generalisation in Drosophila visual learning requires the mushroom bodies. Nature 400, 753–756 (1999)

    Article  Google Scholar 

  108. Loesel, R., Naessel, D., Strausfeld, N.: Common design in a unique midline neuropil in the brains of arthropods. Arthropod Structure and Development 31, 77–91 (2002)

    Article  Google Scholar 

  109. Maass, W., Natschlaeger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)

    Article  MATH  Google Scholar 

  110. Maes, P.: Learning behaviour networks from experience. In: Proceedings of the First European Conference on Artificial Life. MIT Press, Cambridge (1992)

    Google Scholar 

  111. Marr, D.: Vision: a computational investigation into the human representation and processing of visual information. Freeman Publishers, New York (1982)

    Google Scholar 

  112. Martin, J., Ernst, R., Heisenberg, M.: Mushroom bodies suppress locomotor activity in Drosophila melanogaster. Learning and Memory 5, 179–191 (1998)

    Google Scholar 

  113. Martin, J.R., Ernst, R., Heisenberg, M.: Temporal pattern of locomotor activity in Drosophila melanogaster. Journal of Comparative Physiology A 184, 73–84 (1999)

    Article  Google Scholar 

  114. Martin, J.R., Raabe, T., Heisenberg, M.: Central complex substructures are required for the maintenance of locomotor activity in Drosophila melanogaster. Journal of Comparative Physiology A 185, 277–288 (1999)

    Article  Google Scholar 

  115. Mauelshagen, J.: Neural correlates of olfactory learning paradigms in an identified neuron in the honeybee brain. Journal of Neurophysiology 69(2), 609–625 (1993)

    Google Scholar 

  116. McBride, S., Giuliani, G., Chol, C., Krause, P., Correale, D., Watson, K., Baker, G., Siwicki, K.: Mushroom body ablation impairs short-term memory and long-term memory of courtship conditioning in Drosophila melanogaster. Neuron 24, 967–977 (1999)

    Article  Google Scholar 

  117. McFarland, D., Bösser, T.: Intelligent Behavior in Animals and Robots. MIT Press, Cambridge (1993)

    Google Scholar 

  118. Menzel, R.: Searching for the memory trace in a mini-brain, the honeybee. Learning and Memory 8, 53–62 (2001)

    Article  Google Scholar 

  119. Menzel, R., Blakers, M.: Colour receptors in the bee eye - morphology and spectral sensitivity. Journal of Comparative Physiology A: Sensory, Neural, and Behavioural Physiology 108(1), 11–33 (1976)

    Article  Google Scholar 

  120. Menzel, R., Giurfa, M.: Cognitive architecture of a mini-brain: the honeybee. Trends in Cognitive Sciences 5(2), 62–71 (2001)

    Article  Google Scholar 

  121. Metta, G., Fitzpatrick, P.: Better vision through manipulation. Adaptive Behavior 11, 109–128 (2003)

    Article  Google Scholar 

  122. Meyer, J.A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., Berthoz, A.: The psikharpax project: Towards building an artificial rat. Robotics and Autonomous Systems 50, 211–223 (2005)

    Article  Google Scholar 

  123. Mishima, T., Kanzaki, R.: Physiological and morphological characterisation of olfactory descending interneurons of the male silkworm moth, bombyx mori. Journal of Comparative Physiology A 184, 143–160 (1999)

    Article  Google Scholar 

  124. Mizunami, M., Iwasaki, M., Nishikawa, M., Okada, R.: Modular structures in the mushroom body of the cockroach. Neuroscience Letters 229, 153–156 (1997)

    Article  Google Scholar 

  125. Mizunami, M., Okada, R., Li, Y., Strausfeld, N.: Mushroom bodies of the cockroach: activity and identities of neurons recorded in freely moving animals. Journal of Comparative Neurology 402, 501–519 (1998)

    Article  Google Scholar 

  126. Mizunami, M., Weibrecht, J., Strausfeld, N.: Mushroom bodies of the cockroach: their participation in place memory. Journal of Comparative Neurology 402, 520–537 (1998)

    Article  Google Scholar 

  127. Mobbs, P.: The brain and the honeybee Apis mellifera. I. The connections and spatial organization of the mushroom bodies. Philosophical Transactions of the Royal Society B 298, 309–354 (1982)

    Article  Google Scholar 

  128. Mueller, M., Homberg, U., Kuehn, A.: Neuroarchitecture of the lower division of the central body in the brain of the locust (Schistocerca gregaria). Cell Tissue Research 288, 159–176 (1997)

    Article  Google Scholar 

  129. Newell, A., Simon, H.: Computer science as empirical enquiry: symbols and search. Communications of the Association for Computer Machinery 19, 113–126 (1976)

    MathSciNet  Google Scholar 

  130. Nicolis, S., Tsuda, I.: On the parallel between Zipf’s law and 1/f processes in chaotic systems possessing coexisting attractors: a possible mechanism for language formation in the cerebral cortex. Progress of Theoretical Physics 82, 254–274 (1989)

    MathSciNet  Google Scholar 

  131. Nishikawa, M., Nishino, H., Mizunami, M., Yokohari, F.: Function-specific distribution patterns of axon terminals of input neurons in the calyces of the mushroom body of the cockroach, periplaneta americana. Neuroscience Letters 245, 33–36 (1998)

    Article  Google Scholar 

  132. Nolfi, S., Floreano, D.: Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge (2000)

    Google Scholar 

  133. Nowotny, T., Huerta, R., Abarbanel, H., Rabinovich, M.: Self-organization in the olfactory system: one shot odor recognition in insects. Biological Cybernetics 93, 436–446 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  134. O’Donnell, S., Donlan, N., Jones, T.: Mushroom body structural change is associated with division of labor in eusocial wasp workers (Polybia aequatorialis, Hymenoptera: Vespidae). Neuroscience Letters 356, 159–162 (2004)

    Article  Google Scholar 

  135. Okada, R., Ikeda, J., Mizunami, M.: Sensory responses and movement-related activities in extrinsic neurons of the cockroach mushroom bodies. Journal of Comparative Physiology A 185, 115–129 (1999)

    Article  Google Scholar 

  136. Okada, R., Sakura, M., Mizunami, M.: Distribution of dendrites of descending neurons and its implications for the basic organisation of the cockroach brain. Journal of Comparative Neurology 458, 158–174 (2003)

    Article  Google Scholar 

  137. Okajima, K., Tanaka, S., Fujiwara, S.: A heteroassociative memory network with feedback connection. In: Caudill, M., Butler, C. (eds.) Proc. IEEE First International Conference on Neural Networks, pp. 711–718. IEEE, Los Alamitos (1987)

    Google Scholar 

  138. Olshausen, B., Field, D.: Sparse coding of sensory inputs. Current Opinion in Neurobiology 14, 481–487 (2004)

    Article  Google Scholar 

  139. O’Regan, J.K., Noe, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24, 939–1031 (2001)

    Article  Google Scholar 

  140. Paine, R.W., Tani, J.: Motor primitive and sequence self-organization in a hierarchical recurrent neural network. Neural Networks 17, 1291–1309 (2004)

    Article  Google Scholar 

  141. Pascual, A., Preat, T.: Localization of long-term memory within the Drosophila mushroom body. Science 294, 1115–1117 (2001)

    Article  Google Scholar 

  142. Perez-Orive, J., Bazhenov, M., Laurent, G.: Intrinsic and circuit properties favor coincidence detection for decoding oscillatory input. Journal of Neuroscience 24, 6037–6047 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  144. Pfeifer, R., Scheier, C.: Understanding intelligence. The MIT Press, Cambridge (1999)

    Google Scholar 

  145. Philipona, D., O’Regan, J., Nadal, J.P., Coenen, O.: Perception of the structure of the physical world using unknown multimodal sensors and effectors. In: Advances in Neural Information Processing Systems (2004)

    Google Scholar 

  146. Porr, B., Woergoetter, F.: Inside embodiment - what means embodiment to radical constructivists? Kybernetes 34, 105–117 (2005)

    Article  Google Scholar 

  147. Port, R., van Gelder, T. (eds.): Mind as motion: explorations in the dynamics of cognition. A Bradford Book. The MIT Press, Cambridge (1995)

    Google Scholar 

  148. Prescott, T., Redgrave, P., Gurney, K.: Layered control architectures in robots and vertebrates. Adaptive Behavior 7, 99–127 (1999)

    Article  Google Scholar 

  149. Prescott, T.J., Gurney, K., Montes-Gonzalez, F., Humphries, M., Redgrave, P.: The robot basal ganglia: Action selection by an embedded model of the basal ganglia, pp. 349–356. Plenum Press, New York (2002)

    Google Scholar 

  150. Pressing, J.: Referential dynamics of cognition and action. Psychological Review 106, 714–747 (1999)

    Article  Google Scholar 

  151. Prigogine, I.: From being to becoming: time and complexity in the physical sciences. Freeman, NY (1980)

    Google Scholar 

  152. Rieke, F., Warland, D., de Ruyter van Steveninck, R., Bialek, W.: Spikes: exploring the neural code. MIT Press, Cambridge (1997)

    Google Scholar 

  153. Rizzolatti, G., Craighero, L.: The mirror neuron system. Annual Review of Neuroscience 27, 169–192 (2004)

    Article  Google Scholar 

  154. Rock, I.: In defense of unconscious inference. Wiley, New York (1997)

    Google Scholar 

  155. van Rooij, I., Bongers, R., Haselager, W.: A non-representational approach to imagined action. Cognitive Science 26, 345–375 (2002)

    Article  Google Scholar 

  156. Rosay, P., Armstrong, D., Wang, Z., Kaiser, K.: Synchronized neural activity in the Drosophila memory centres and its modulation by amnesiac. Neuron 30, 759–770 (2001)

    Article  Google Scholar 

  157. Rosenblatt, J.K., Payton, D.W.: A fine-grained alternative to the subsumption architecture for mobile robot control. In: Proc. of the IEEE Int. Conf. on Neural Networks, vol. 2, pp. 317–324. IEEE Press, Washington (1989)

    Google Scholar 

  158. Rosenblum, L.: Acoustical information for controlled collisions. In: Schick, A. (ed.) Contributions to Psychological Acoustics. Bibliotheks- und Informationssystem der Carl von Ossietzky Universitaet Oldenburg, Oldenburg (1993)

    Google Scholar 

  159. Rosenblum, L., Wuestefeld, A., Anderson, K.: Auditory reachability: an affordance approach to the perception of sound source distance. Ecological Psychology 8, 1–24 (1996)

    Article  Google Scholar 

  160. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  161. Rybak, J., Menzel, R.: Anatomy of the mushroom bodies in the honey bee brain: the neuronal connections of the alpha-lobe. Journal of Comparative Neurology 334, 444–465 (1993)

    Article  Google Scholar 

  162. Schildberger, K.: Multimodal interneurons in the cricket brain: properties of identified extrinsic mushroom body cells. Journal of Comparative Physiology A 154, 71–79 (1984)

    Article  Google Scholar 

  163. Schoener, G., Dijkstra, T., Jeka, J.: Action-perception patterns emerge from coupling and adaptation. Ecological Psychology 10, 323–346 (1998)

    Article  Google Scholar 

  164. Schöner, G., Dose, M., Engels, C.: Dynamics of behaviour: theory and applications for autonomous robot architectures. Robotics and Autonomous Systems 16, 213–245 (1995)

    Article  Google Scholar 

  165. Schuermann, F.W.: Bemerkungen zur Funktion der Corpora pedunculata im Gehirn der Insekten aus morphologischer Sicht. Experimental Brain Research 19, 406–432 (1974)

    Google Scholar 

  166. Schultz, W., Dayan, P., Montague, P.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)

    Article  Google Scholar 

  167. Schultz, W., Dickinson, A.: Neuronal coding of prediction errors. Annual Review of Neuroscience 23, 473–500 (2000)

    Article  Google Scholar 

  168. Schwaerzel, M., Monastirioti, M., Scholz, H., Friggi-Grelin, F., Birman, S., Heisenberg, M.: Dopamine and octopamine differentiate between aversive and appetitive olfactory memories in Drosophila. Journal of Neuroscience 23(33), 10495–10502 (2003)

    Google Scholar 

  169. Seth, A.: Evolving action selection and selective attention without actions. In: Pfeiffer, R. (ed.) From Animals to Animats 5, Proc. of 5th Intl. Conf. on Simulation of Adaptive Behavior. MIT Press/Bradford Books (1998)

    Google Scholar 

  170. Skarda, C., Freeman, W.: How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences 10, 161–173 (1987)

    Article  Google Scholar 

  171. Smithers, T.: On behaviour as dissipative structures in agent-environment interaction systems. In: Ritter, H., Cruse, H., Dean, J. (eds.) Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, vol. 2, pp. 243–257. Kluwer Academic Pulishers, Dordrecht (2000)

    Google Scholar 

  172. Stange, G., Stowe, S., Chahl, J., Massaro, A.: Anisotropic imaging in the dragonfly median ocellus: a matched filter for horizon detection. Journal of Comparative Physiology A 188, 455–467 (2002)

    Article  Google Scholar 

  173. Steels, L.: Synthesising the origins of language and meaning using co-evolution, self-organisation and level formation. In: Evolution of Human Language. Edinburgh Univ. Press (1996)

    Google Scholar 

  174. Stopfer, M., Bhagavan, S., Smith, B., Laurent, G.: Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 390, 70–74 (1997)

    Article  Google Scholar 

  175. Strausfeld, N.: Structural organization of male-specific visual neurons in Calliphorid optic lobes. Journal of Comparative Physiology A 169, 379–393 (1991)

    Article  Google Scholar 

  176. Strausfeld, N.: A brain region in insects that supervises walking. Progress in Brain Research 123, 273–284 (1999)

    Article  Google Scholar 

  177. Strausfeld, N., Hansen, L., Li, Y., Gomez, R., Ito, K.: Evolution, discovery, and interpretations of arthropod mushroom bodies. Learning and Memory 5, 11–37 (1998)

    Google Scholar 

  178. Strausfeld, N., Li, Y.: Representation of the calyces in the medial and vertical lobes of cockroach mushroom bodies. Journal of Comparative Neurology 409, 626–646 (1999)

    Article  Google Scholar 

  179. Strauss, R.: The central complex and the genetic dissection of locomotor behaviour. Current Opinion in Neurobiology 12, 633–638 (2002)

    Article  Google Scholar 

  180. Strauss, R., Pichler, J.: Persistence of orientation toward a temporarily invisible landmark in Drosophila melanogaster. Journal of Comparative Physiology A 182, 411–423 (1998)

    Article  Google Scholar 

  181. Tani, J., Ito, M.: Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment. IEEE Trans. on Systems, Man, and Cybernetics Part A: Systems and Humans 33, 481–488 (2003)

    Article  Google Scholar 

  182. Thelen, E., Smith, L.: A dynamic systems approach to the development of cognition and action. MIT Press, Cambridge (1994)

    Google Scholar 

  183. Tuller, B., Case, P., Ding, M., Kelso, J.: The nonlinear dynamics of speech categorization. Journal of Experimental Psychology: Human Perception and Performance 20, 3–11 (1994)

    Article  Google Scholar 

  184. Turvey, M.T.: Dynamic touch. American Psychologist 5, 1134–1152 (1996)

    Article  Google Scholar 

  185. Tyrrell, T.: Computational mechanisms for action selection. Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh (1993)

    Google Scholar 

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

    Article  Google Scholar 

  187. Vitzthum, H., Mueller, M., Homberg, U.: Neurons of the central complex of the locust Schistocerca gregaria are sensitive to polarised light. The Journal of Neuroscience 22(3), 1114–1125 (2002)

    Google Scholar 

  188. Waddell, S., Armstrong, D., Kitamoto, T., Kaiser, K., Quinn, W.: The amnesiac gene product is expressed in two neurons in the Drosophila brain that are critical for memory. Cell 103, 805–813 (2000)

    Article  Google Scholar 

  189. Waddell, S., Quinn, W.: Flies, genes and learning. Annual Review of Neuroscience 24, 1283–1309 (2001)

    Article  Google Scholar 

  190. Wann, J.: Anticipating arrival: Is the tau margin a specious theory? Journal of Experimental Psychology: Human Perception and Performance 22, 1031–1048 (1996)

    Article  Google Scholar 

  191. Webb, B.: Can robots make good models of biological behaviour? Behavioral and Brain Sciences 24, 1033–1050 (2001)

    Google Scholar 

  192. Webb, B.: Neural mechanisms for prediction: do insects have forward models? Trends in neuroscience 27(5), 278–282 (2004)

    Article  Google Scholar 

  193. Wehner, R.: ‘Matched filters’ - neural models of the external world. Journal of Comparative Physiology 161, 511–531 (1987)

    Article  Google Scholar 

  194. Wehner, R.: Desert ant navigation: how miniature brains solve complex tasks. Journal of Comparative Physiology A 189, 579–588 (2003)

    Article  Google Scholar 

  195. Weiner, J.: On the practice of ecology. Journal of Ecology 83, 153–158 (1995)

    Article  Google Scholar 

  196. Wessnitzer, J., Webb, B.: Multimodal sensory integration in insects - towards insect brain control architectures. Bioinspiration and Biomimetics 1, 63–75 (2006)

    Article  Google Scholar 

  197. Wexler, M., Kosslyn, S., Berthoz, A.: Motor processes in mental rotation. Cognition 68, 77–94 (1998)

    Article  Google Scholar 

  198. Wilson, M.: Six views of embodied cognition. Psychological Bulletin and Review 9, 625–636 (2002)

    Google Scholar 

  199. Wittmann, T., Schwegler, H.: Path integration - a network model. Biological Cybernetics 73, 569–575 (1995)

    Article  MATH  Google Scholar 

  200. Woergoetter, F., Porr, B.: Temporal sequence learning, prediction and control - a review of different models and their relation to biological mechanisms. Neural Computation 17, 245–319 (2005)

    Article  Google Scholar 

  201. Wohlgemuth, S., Ronacher, B., Wehner, R.: Ant odometry in the third dimension. Nature 411, 795–798 (2001)

    Article  Google Scholar 

  202. Wolpert, D., Ghahramani, Z.: Computational principles of movement neuroscience. Nature Neuroscience 3, 1212–1217 (2000)

    Article  Google Scholar 

  203. Wolpert, D., Ghahramani, Z., Jordan, M.: An internal model for sensorimotor integration. Science 269, 1880–1882 (1995)

    Article  Google Scholar 

  204. Wuestenberg, D., Boytcheva, M., Gruenewald, B., Byrne, J., Menzel, R., Baxter, D.: Current- and voltage-clamp recordings and computer simulations of Kenyon cells in the honeybee. Journal of Neurophysiology 92, 2589–2603 (2004)

    Article  Google Scholar 

  205. Wyley, D., Bischof, W., Frost, B.: Common reference frame for neural coding of translational and rotational optic flow. Nature 392, 278–282 (1998)

    Article  Google Scholar 

  206. Yusuyama, K., Meinertzhagen, I., Schuermann, F.W.: Synaptic organization of the mushroom body calyx in Drosophila melanogaster. Journal of Comparative Neurology 445, 211–226 (2002)

    Article  Google Scholar 

  207. Zeil, J., Hofmann, M., Chahl, J.: Catchment areas of panoramic snapshots in outdoor scenes. Journal of the Optical Society of America A 20, 450–469 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Perception, Action and Behaviour, University of Edinburgh,  

    B. Webb & J. Wessnitzer

Authors
  1. B. Webb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Wessnitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Università di Catania, Catania, Italy

    Paolo Arena  & Luca Patanè  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Webb, B., Wessnitzer, J. (2009). Perception for Action in Insects. In: Arena, P., Patanè, L. (eds) Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots. Cognitive Systems Monographs, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88464-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88464-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88463-7

  • Online ISBN: 978-3-540-88464-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics