Skip to main content

Evolution of Small-World Properties in Embodied Networks

  • Conference paper
  • 1714 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7366)

Abstract

The ontogenetic process that forms the structure of biological brains is believed to be ruled primarily by optimizing principles of resource allocation and constraint satisfaction for resources such as metabolic energy, wiring length, cranial volume, etc. These processes lead to networks that have interesting macroscopic structures, such as small-world and scale-free organization. However, open questions remain about the importance of these structures in cognitive performance, and how information processing constraints might provide requirements that dictate the types of macro structures observed. Constraints on the physical and metabolic needs of biological brains must be balanced with information processing constraints. It is therefore plausible that observed structures of biological brains are the result of both physical and information processing needs. In this paper we show that small-world structure can evolve under combined physical and functional constraints for a simulated evolution of a neuronal controller for an embodied agent in a navigational task.

Keywords

  • small-world networks
  • embodied cognition
  • evolutionary development

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • Compact, lightweight 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U.: Complex networks: structure and dynamics. Physics Reports 424, 175–308 (2006)

    CrossRef  MathSciNet  Google Scholar 

  2. Koch, C., Laurent, G.: Complexity and the nervous system. Science 284, 96–98 (1999)

    CrossRef  Google Scholar 

  3. Stephan, K.E., Hilgetag, C.-C., Burns, G.A.P.C., O’Neill, M.A., Young, M.P., Kötter, R.: Computational analysis of functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society of London B 355, 111–126 (2000)

    CrossRef  Google Scholar 

  4. Sporns, O., Tononi, G., Edelman, G.M.: Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex 10, 127–141 (2000)

    CrossRef  Google Scholar 

  5. Bassett, D.S., Bullmore, E.: Small-World brain networks. The Neuroscientist 12(6), 512–523 (2006)

    CrossRef  Google Scholar 

  6. Chklovskii, D.B., Schikorski, T., Stevens, C.F.: Wiring optimization in cortical circuits. Neuron 34, 341–347 (2002)

    CrossRef  Google Scholar 

  7. Sporns, O., Tononi, G., Edelman, G.M.: Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks 13, 909–922 (2000)

    CrossRef  Google Scholar 

  8. Kaiser, M., Hilgetag, C.C.: Modelling the development of cortical systems networks. Neurocomputing 58-60, 297–302 (2004)

    CrossRef  Google Scholar 

  9. Simard, D., Nadeau, L., Kröger, H.: Fastest learning in small-world neural networks. Phys. Letters A 336, 8–22 (2005)

    CrossRef  MATH  Google Scholar 

  10. Kim, B.J.: Performance of networks of artificial neurons: The role of clustering. Physical Review E 69(4), 45101 (2004)

    CrossRef  Google Scholar 

  11. McGraw, P.N., Menzinger, M.: Topology and computational performance of attractor neural networks. Physical Review E 68(4), 047102 (2003)

    CrossRef  Google Scholar 

  12. Ahn, Y.-Y., Jeong, H., Kim, B.J.: Wiring cost in the organization of a biological neuronal network. Physica A: Statistical Mechanics and its Applications 367, 531–537 (2006)

    CrossRef  Google Scholar 

  13. Davey, N., Christianson, B., Adams, R.: High capacity associative memories and small world networks. Neural Networks 4, 177–182 (2004)

    Google Scholar 

  14. Lavond, D.G., Steinmetz, J.E.: Handbook of Classical Conditioning. Kluwer Academic Publishers, Norwell (2003)

    CrossRef  Google Scholar 

  15. Gerkey, B., Vaughan, R.T., Howard, A.: The player/stage project: Tools for multi-robot and distributed sensor systems. In: Proceedings of the 11th International Conference on Advanced Robotics (ICAR 2003), Coimbra, Portugal, pp. 317–323 (June 2003)

    Google Scholar 

  16. Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C.: Organization, development and function of complex brain networks. Trends in Cognitive Sciences 8(9), 418–425 (2004)

    CrossRef  Google Scholar 

  17. Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Harter, D. (2012). Evolution of Small-World Properties in Embodied Networks. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31561-9_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31560-2

  • Online ISBN: 978-3-642-31561-9

  • eBook Packages: Computer ScienceComputer Science (R0)