Skip to main content
SpringerLink
Log in

Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks

  • Chapter
  • First Online:
Growing Adaptive Machines

Part of the book series: Studies in Computational Intelligence ((SCI,volume 557))

  • 1678 Accesses

Abstract

In nature, brains are built through a process of biological development in which many aspects of the network of neurons and connections change and are shaped by external information received through sensory organs. From numerous studies in neuroscience, it has been demonstrated that developmental aspects of the brain are intimately involved in learning. Despite this, most artificial neural network (ANN) models do not include developmental mechanisms and regard learning as the adjustment of connection weights. Incorporating development into ANNs raises fundamental questions. What level of biological plausibility should be employed? In this chapter, we discuss two artificial developmental neural network models with differing degrees of biological plausibility. One takes the view that the neuron is fundamental (neuro-centric) so that all evolved programs are based at the level of the neuron, the other carries out development at an entire network level and evolves rules that change the network (holocentric). In the process, we hope to reveal some important issues and questions that are relevant to researchers wishing to create other such models.

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

Access this chapter

Log in via an institution
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

Notes

  1. 1.

    However, a recent study showed that environmental enrichment alone does not significantly increase hippocampal neurogenesis or bestow spatial learning benefits in mice [17].

  2. 2.

    other reported implementations of SMCGP use floating point numbers.

  3. 3.

    This work has not involved ANNs.

References

  1. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophy. 5, 115–133 (1943)

    Google Scholar 

  2. E.R. Kandel, J.H. Schwartz, T.M. Jessell, Principles of Neural Science, 4th edn. (McGraw-Hill, New York, 2000)

    Google Scholar 

  3. R.M. French, Catastrophic forgetting in connectionist networks: causes, consequences and solutions. Trends Cogn. Sci. 3(4), 128–135 (1999)

    Article  MathSciNet  Google Scholar 

  4. M. McCloskey, N.J. Cohen, Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. 24, 109–165 (1989)

    Article  Google Scholar 

  5. R. Ratcliff, Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychol. Rev. 97, 285–308 (1990)

    Article  Google Scholar 

  6. S. Judd, On the complexity of loading shallow neural networks. J. Complex. 4, 177–192 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  7. E.B. Baum, A proposal for more powerful learning algorithms. Neural Comput. 1, 201–207 (1989)

    Article  Google Scholar 

  8. S.E. Fahlman, C. Lebiere, The cascade-correlation architecture, ed. by D.S. Touretzky. Advances in Neural Information Processing Systems (Morgan Kaufmann, San Mateo, 1990)

    Google Scholar 

  9. M. Frean, The upstart algorithm: a method for constructing and training feedforward neural networks. Neural Comput. 2, 198–209 (1990)

    Article  Google Scholar 

  10. P.T. Quinlan, Structural change and development in real and artificial networks. Neural Netw. 11, 577–599 (1998)

    Article  Google Scholar 

  11. P.T. Quinlan (ed.), Connectionist Models of Development (Psychology Press, New York, 2003)

    Google Scholar 

  12. J.F. Miller, G.M. Khan, Where is the brain inside the brain? on why artificial neural networks should be developmental. Memet. Comput. 3(3), 217–228 (2011)

    Article  Google Scholar 

  13. J.R. Smythies, The Dynamic Neuron (MIT Press, Cambridge, 2002)

    Google Scholar 

  14. F. Valverde, Rate and extent of recovery from dark rearing in the visual cortex of the mouse. Brain Res. 33, 1–11 (1971)

    Article  Google Scholar 

  15. J.A. Kleim, E. Lussnig, E.R. Schwartz, T.A. Comery, W.T. Greenough, Synaptogenesis and fos expression in the motor cortex of the adult rat after motor skill learning. J. Neurosci 16, 4529–4535 (1996)

    Google Scholar 

  16. J.A. Kleim, K. Vij, D.H. Ballard, W.T. Greenough, Learning-dependent synaptic modifications in the cerebellar cortex of the adult rat persist for at least four weeks. J. Neurosci 17, 717–721 (1997)

    Google Scholar 

  17. M.L. Mustroph, S. Chen, S.C. Desai, E.B. Cay, E.K. Deyoung, J.S. Rhodes. Aerobic exercise is the critical variable in an enriched environment that increases hippocampal neurogenesis and water maze learning in male C57BL/6J mice. Neuroscience (2012), Epub ahead of print

    Google Scholar 

  18. A.D. Tramontin, E. Brenowitz, Seasonal plasticity in the adult brain. Trends Neurosci. 23, 251–258 (2000)

    Article  Google Scholar 

  19. E.A. Maguire, D.G. Gadian, I.S. Johnsrude, C.D. Good, J. Ashburner, R.S.J. Frackowiak, C.D. Frith, Navigation-related structural change in the hippocampi of taxi drivers. PNAS 97, 4398–4403 (2000)

    Article  Google Scholar 

  20. S. Rose, The Making of Memory: From Molecules to Mind (Vintage, London, 2003)

    Google Scholar 

  21. A.S. Dekaban, D. Sadowsky, Changes in brain weights during the span of human life. Ann. Neurol. 4, 345–356 (1978)

    Article  Google Scholar 

  22. G.M. Khan, J.F. Miller, Evolution of cartesian genetic programs capable of learning, ed. by F. Rothlauf. Conference on Genetic and Evolutionary Computation (GECCO) (ACM, 2009) pp. 707–714

    Google Scholar 

  23. G.M. Khan, J.F. Miller, D.M. Halliday, Coevolution of intelligent agents using Cartesian genetic programming. Conference on Genetic and Evolutionary Computation (GECCO) (2007), pp. 269–276

    Google Scholar 

  24. G.M. Khan, J.F. Miller, D.M. Halliday, Breaking the synaptic dogma: Evolving a neuro-inspired developmental network, ed. by X. Li, M. Kirley, M. Zhang, D.G. Green, V. Ciesielski, H.A. Abbass, Z. Michalewicz, T. Hendtlass, K. Deb, K.C. Tan, J. Branke, Y. Shi. Simulated Evolution and Learning, 7th International Conference, SEAL 2008, Melbourne, Australia, December 7–10, 2008. Proceedings, volume 5361 of Lecture Notes in Computer Science (Springer, 2008), pp. 11–20

    Google Scholar 

  25. G.M. Khan, J.F. Miller, D.M. Halliday, Coevolution of neuro-developmental programs that play checkers, ed. by G. Hornby, L. Sekanina, P.C. Haddow. Evolvable Systems: From Biology to Hardware, 8th International Conference, ICES 2008, Prague, Czech Republic, September 21–24, 2008. Proceedings, volume 5216 of Lecture Notes in Computer Science (Springer, 2008), pp. 352–361

    Google Scholar 

  26. G.M. Khan, J.F. Miller, D.M. Halliday, Developing neural structure of two agents that play checkers using cartesian genetic programming, ed. by C. Ryan, M. Keijzer. Conference on Genetic and Evolutionary Computation (GECCO) Companion Material (ACM, 2008), pp. 2169–2174

    Google Scholar 

  27. G.M. Khan, J.F. Miller, D.M. Halliday, In search of intelligent genes: the cartesian genetic programming computational neuron (cgpcn), in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18–21 May, 2009 (IEEE, 2009), pp. 574–581

    Google Scholar 

  28. G.M. Khan, J.F. Miller, D.M. Halliday, Evolution of cartesian genetic programs for development of learning neural architecture. Evol. Comput. 19(3), 469–523 (2011)

    Article  Google Scholar 

  29. K.O. Stanley, D.B. D’Ambrosio, J. Gauci, A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15, 185–212 (2009)

    Article  Google Scholar 

  30. F. Gruau, Automatic definition of modular neural networks. Adapt. Behav. 3, 151–183 (1994)

    Article  Google Scholar 

  31. J.R. Koza, Genetic Programming: On the Programming of Computers by Natural Selection (MIT Press, Cambridge, 1992)

    MATH  Google Scholar 

  32. J.F. Miller, An Empirical Study of the Efficiency of Learning Boolean Functions using a Cartesian Genetic Programming Approach. Conference on Genetic and Evolutionary Computation (GECCO) (Morgan Kaufmann, 1999), pp. 1135–1142

    Google Scholar 

  33. J.F. Miller, P. Thomson, Cartesian Genetic Programming, in Proceedings of the European Conference on Genetic Programming, vol. 1802 of LNCS (Springer, 2000), pp. 121–132

    Google Scholar 

  34. S. Harding, J.F. Miller, W. Banzhaf, A survey of self modifying CGP, ed. by R. Riolo, T. McConaghy, E. Vladislavleda. Genetic Programming Theory and Practice VIII, 2010 (Springer, 2010) pp. 91–107

    Google Scholar 

  35. S. Harding, J.F. Miller, W. Banzhaf, Self-modifying Cartesian Genetic Programming, in Proceedings of the Genetic and Evolutionary Computation Conference (2007), pp. 1021–1028

    Google Scholar 

  36. S. Harding, J.F. Miller, W. Banzhaf, Evolution, development and learning using self-modifying cartesian genetic programming, ed. by F. Rothlauf. Conference on Genetic and Evolutionary Computation (GECCO) (ACM, 2009), pp. 699–706

    Google Scholar 

  37. S. Harding, J.F. Miller, W. Banzhaf, Self modifying cartesian genetic programming: Fibonacci, squares, regression and summing, ed. by L. Vanneschi, S. Gustafson, A. Moraglio, I. De Falco, M. Ebner. Genetic Programming, 12th European Conference, EuroGP 2009, Tübingen, Germany, April 15–17, 2009, Proceedings, volume 5481 of Lecture Notes in Computer Science (Springer, 2009), pp. 133–144

    Google Scholar 

  38. S. Harding, J.F. Miller, W. Banzhaf, Self modifying cartesian genetic programming: Parity, in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18–21 May, IEEE, 2009 (2009), pp. 285–292

    Google Scholar 

  39. S. Harding, J.F. Miller, W. Banzhaf, Developments in cartesian genetic programming: self-modifying cgp. Genet. Program. Evolvable Mach. 11(3–4), 397–439 (2010)

    Article  Google Scholar 

  40. S. Harding, J.F. Miller, W. Banzhaf, Self modifying cartesian genetic programming: finding algorithms that calculate pi and e to arbitrary precision, ed. by M. Pelikan, J. Branke. Conference on Genetic and Evolutionary Computation (GECCO) (ACM, 2010), pp. 579–586

    Google Scholar 

  41. M.M. Khan, G.M. Khan, J.F. Miller, Efficient representation of recurrent neural networks for Markovian/Non-Markovian non-linear control problems, ed. by A.E. Hassanien, A. Abraham, F. Marcelloni, H. Hagras, M. Antonelli, T.-P. Hong, in Proceedings of the International Conference on Intelligent Systems Design and Applications (IEEE, 2010), pp. 615–620

    Google Scholar 

  42. M.M. Khan, G.M. Khan, J.F. Miller, Evolution of neural networks using cartesian genetic programming, in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18–23 July 2010 (IEEE, 2010)

    Google Scholar 

  43. M.M Khan, G.M. Khan, J.F. Miller, Evolution of optimal anns for non-linear control problems using cartesian genetic programming, ed. by H.R. Arabnia, D. de la Fuente, E.B. Kozerenko, J.A. Olivas, R. Chang, P.M. LaMonica, R.A. Liuzzi, A.M.G. Solo, in Proceedings of the 2010 International Conference on Artificial Intelligence, ICAI 2010, July 12–15, 2010, Las Vegas Nevada, USA, vol. 2 (CSREA Press, 2010), pp. 339–346

    Google Scholar 

  44. J.A. Walker, J.F. Miller, The automatic acquisition, evolution and reuse of modules in cartesian genetic programming. IEEE Trans. Evolut. Comput. 12(4), 397–417 (2008)

    Article  Google Scholar 

  45. J.F. Miller (ed.), Cartesian Genetic Programming. Natural Computing Series (Springer, Berlin, 2011)

    Google Scholar 

  46. I. Rechenberg, Evolutionsstrategie - Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Ph.D. thesis, Technical University of Berlin, Germany, (1971)

    Google Scholar 

  47. J.F. Miller, S.L. Smith, Redundancy and computational efficiency in cartesian genetic programming. IEEE Trans. Evolut. Comput. 10(2), 167–174 (2006)

    Article  Google Scholar 

  48. V.K. Vassilev, J.F. Miller, The Advantages of Landscape Neutrality in Digital Circuit Evolution. International Conference on Evolvable Systems, vol. 1801 of LNCS (Springer, 2000), pp. 252–263

    Google Scholar 

  49. T. Yu, J.F. Miller, Neutrality and the evolvability of Boolean function landscape, in Proceedings of the European Conference on Genetic Programming, vol. 2038 of LNCS (Springer, 2001), pp. 204–217

    Google Scholar 

  50. G.M. Khan, J.F Miller, Solving mazes using an artificial developmental neuron, in Proceedings of the Conference on Artificial Life (ALIFE) XII (MIT Press, 2010), pp. 241–248

    Google Scholar 

  51. S. Harding, J.F. Miller, W. Banzhaf, SMCGP2: Self-modifying Cartesian Genetic Programming in Two Dimensions. Conference on Genetic and Evolutionary Computation (GECCO) (ACM, 2011), pp. 1491–1498

    Google Scholar 

  52. W. Gerstner, W.M. Kistler, Spiking Neuron Models (Cambridge University Press, Cambridge, 2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics, University of York, York, UK

    Julian F. Miller

Authors
  1. Julian F. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julian F. Miller .

Editor information

Editors and Affiliations

  1. CNRS, Institut des Systèmes Complexes - Paris Île-de-France, Paris, France

    Taras Kowaliw

  2. Institute of Intelligent Systems and Robotics, CNRS UMR 7222, Université Pierre et Marie Curie, Paris, France

    Nicolas Bredeche

  3. School of Biomedical Engineering, Drexel University, Philadelphia, Pennsylvania, USA

    René Doursat

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Miller, J.F. (2014). Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks. In: Kowaliw, T., Bredeche, N., Doursat, R. (eds) Growing Adaptive Machines. Studies in Computational Intelligence, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55337-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-55337-0_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55336-3

  • Online ISBN: 978-3-642-55337-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation