Artificial Neural Networks as Models of Robustness in Development and Regeneration: Stability of Memory During Morphological Remodeling

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 628)

Abstract

Artificial neural networks are both a well-established tool in machine learning and a mathematical model of distributed information processing. Developmental and regenerative biology is in desperate need of conceptual models to explain how some species retain memories despite drastic reorganization, remodeling, or regeneration of the brain. Here, we formalize a method of artificial neural network perturbation and quantitatively analyze memory persistence during different types of topology change. We introduce this system as a computational model of the complex information processing mechanisms that allow memories to persist during significant cellular and morphological turnover in the brain. We found that perturbations in artificial neural networks have a general negative effect on the preservation of memory, but that the removal of neurons with different firing patterns can effectively minimize this memory loss. The training algorithms employed and the difficulty of the pattern recognition problem tested are key factors determining the impact of perturbations. The results show that certain perturbations, such as neuron splitting and scaling, can achieve memory persistence by functional recovery of lost patterning information. The study of models integrating both growth and reduction, combined with distributed information processing is an essential first step for a computational theory of pattern formation, plasticity, and robustness.

Notes

Acknowledgments

We thank the Levin lab, Francisco J. Vico, and many others in the community for helpful discussions at the intersection of neuroscience and developmental biology. This work was supported by NSF (subaward #CBET-0939511 via EBICS at MIT), the G. Harold and Leila Y. Mathers Charitable, and Templeton World Charity Foundations.

References

  1. 1.
    D. Blackiston, T. Shomrat, M. Levin, The stability of memories during brain remodeling: a perspective, Communicative and Integrative Biology (In Press, 2015)Google Scholar
  2. 2.
    J.V. McConnell, A.L. Jacobson, D.P. Kimble, The effects of regeneration upon retention of a conditioned response in the planarian. J. Comp. Physiol. Psychol. 52, 1–5 (1959)CrossRefGoogle Scholar
  3. 3.
    D.J. Blackiston, E. Silva Casey, M.R. Weiss, Retention of memory through metamorphosis: can a moth remember what it learned as a caterpillar?. PLoS ONE 3, e1736 (2008)Google Scholar
  4. 4.
    J.M. Mateo, Self-referent phenotype matching and long-term maintenance of kin recognition. Anim. Behav. 80, 929–935 (2010)CrossRefGoogle Scholar
  5. 5.
    K.J. Anil, Artificial neural networks: a tutorial (1996), pp. 31–44, http://doi.ieeecomputersociety.org/10.1109/2.485891
  6. 6.
    H. White, Artificial Neural Networks: Approximation and Learning Theory (Blackwell Publishers, Inc., 1992)Google Scholar
  7. 7.
    P. Arlotta, B. Berninger, Brains in metamorphosis: reprogramming cell identity within the central nervous system. Curr. Opin. Neurobiol. 27, 208–214 (2014)CrossRefGoogle Scholar
  8. 8.
    D.A. Berg, L. Belnoue, H. Song, A. Simon, Neurotransmitter-mediated control of neurogenesis in the adult vertebrate brain. Development 140, 2548–2561 (2013)CrossRefGoogle Scholar
  9. 9.
    M. Koehl, D.N. Abrous, A new chapter in the field of memory: adult hippocampal neurogenesis. Eur. J. Neurosci. 33, 1101–1114 (2011)CrossRefGoogle Scholar
  10. 10.
    W. Deng, J.B. Aimone, F.H. Gage, New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory? Nat. Rev. Neurosci. 11, 339–350 (2010)CrossRefGoogle Scholar
  11. 11.
    Y. Kitabatake, K.A. Sailor, G.L. Ming, H. Song, Adult neurogenesis and hippocampal memory function: new cells, more plasticity, new memories?. Neurosurg. Clin. North Am. 18, 105–13 (2007)Google Scholar
  12. 12.
    S. Couillard-Despres, B. Iglseder, L. Aigner, Neurogenesis, cellular plasticity and cognition: the impact of stem cells in the adult and aging brain–a mini-review. Gerontology 57, 559–564 (2011)CrossRefGoogle Scholar
  13. 13.
    C. Wiltrout, B. Lang, Y. Yan, R.J. Dempsey, R. Vemuganti, Repairing brain after stroke: A review on post-ischemic neurogenesis. Mech. Neurodegeneration 50, 1028–1041 (2007)Google Scholar
  14. 14.
    T. Tully, V. Cambiazo, L. Kruse, Memory through metamorphosis in normal and mutant Drosophila. J. Neurosci. 14, 68–74 (1994)Google Scholar
  15. 15.
    M. Gandolfi, L. Mattiacci, S. Dorn, Preimaginal learning determines adult response to chemical stimuli in a parasitic wasp. Proc. R. Soc. Lond. Ser. B-Biol. Sci. 270, 2623–2629 (2003)CrossRefGoogle Scholar
  16. 16.
    K. Rietdorf, J.L.M. Steidle, Was Hopkins right? Influence of larval and early adult experience on the olfactory response in the granary weevil Sitophilus granarius (Coleoptera, Curculionidae). Physiological Entomol. 27, 223–227 (2002)CrossRefGoogle Scholar
  17. 17.
    K. Agata, Y. Umesono, Brain regeneration from pluripotent stem cells in planarian. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 2071–2078 (2008)CrossRefGoogle Scholar
  18. 18.
    T. Shomrat, M. Levin, An automated training paradigm reveals long-term memory in planarians and its persistence through head regeneration. J. Expe. Biol. 216, 3799–3810 (2013)CrossRefGoogle Scholar
  19. 19.
    R.A. Fricker, M.K. Carpenter, C. Winkler, C. Greco, M.A. Gates, A. Björklund, Site-specific migration and neuronal differentiation of human neural progenitor cells after transplantation in the Adult Rat Brain. J. Neurosci. 19, 5990–6005 (1999)Google Scholar
  20. 20.
    A. Wennersten, X. Meijer, S. Holmin, L. Wahlberg, T. Mathiesen, Proliferation, migration, and differentiation of human neural stem/progenitor cells after transplantation into a rat model of traumatic brain injury. J. Neurosurg. 100, 88–96 (2004)CrossRefGoogle Scholar
  21. 21.
    K.G. Akers, A. Martinez-Canabal, L. Restivo, A.P. Yiu, A. De Cristofaro, H.-L. Hsiang et al., Hippocampal neurogenesis regulates forgetting during adulthood and infancy. Science 344, 598–602 (2014)CrossRefGoogle Scholar
  22. 22.
    L.J. Martin, Neuronal death in amyotrophic lateral sclerosis is apoptosis: possible contribution of a programmed cell death mechanism. J. Neuropathol. Exp. Neurol. 58, 459–471 (1999)CrossRefGoogle Scholar
  23. 23.
    W.M. Cowan, J.W. Fawcett, D.D. O’Leary, B.B. Stanfield, Regressive events in neurogenesis. Science 225, 1258–1265 (1984)CrossRefGoogle Scholar
  24. 24.
    Y. Xiong, A. Mahmood, M. Chopp, Angiogenesis, neurogenesis and brain recovery of function following injury. Curr. Opin. Investig. Drugs (Lond., Engl.: 2000) 11, 298–308 (2010)Google Scholar
  25. 25.
    I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3–31 (2000)Google Scholar
  26. 26.
    M. Anthony, P.L. Bartlett, Neural Network Learning: Theoretical Foundations (Cambridge University Press, 2009)Google Scholar
  27. 27.
    S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, 1998)Google Scholar
  28. 28.
    A.M. Hermundstad, K.S. Brown, D.S. Bassett, J.M. Carlson, Learning, memory, and the role of neural network architecture. PLoS Comput. Biol. 7, e1002063 (2011)MathSciNetCrossRefGoogle Scholar
  29. 29.
    P.G. Benardos, G.C. Vosniakos, Optimizing feedforward artificial neural network architecture. Eng. Appl. Artif. Intell. 20, 365–382 (2007)CrossRefGoogle Scholar
  30. 30.
    M.M. Islam, M.A. Sattar, M.F. Amin, K. Murase, A new adaptive strategy for pruning and adding hidden neurons during training artificial neural networks, in Intelligent Data Engineering and Automated Learning—IDEAL 2008, vol. 5326, ed. by C. Fyfe, D. Kim, S.-Y. Lee, H. Yin (Springer Berlin Heidelberg, 2008), pp. 40–48Google Scholar
  31. 31.
    Y. Lecun, J.S. Denker, S.A. Solla, Optimal Brain Damage, pp. 598–605Google Scholar
  32. 32.
    K.O. Stanley, R. Miikkulainen, Efficient reinforcement learning through evolving neural network topologies. Network (Phenotype) 1, 3 (1996)Google Scholar
  33. 33.
    A.N. Hampton, C. Adami, Evolution of robust developmental neural networks. Proc. Artif. Life 9, 438–443 (2004)Google Scholar
  34. 34.
    J.F. Miller, Evolving developmental programs for adaptation, morphogenesis, and self-repair, in Advances in Artificial Life (Springer, 2003), pp. 256–265Google Scholar
  35. 35.
    J.C. Astor, C. Adami, A developmental model for the evolution of artificial neural networks. Artif. Life 6, 189–218 (2000)CrossRefGoogle Scholar
  36. 36.
    J.E. Auerbach, J.C. Bongard, Evolving CPPNs to grow three-dimensional physical structures, in Proceedings of the 12th Annual Conference on GENETIC and Evolutionary Computation (2010), pp. 627–634Google Scholar
  37. 37.
    N. Bessonov, M. Levin, N. Morozova, N. Reinberg, A. Tosenberger, V. Volpert, On a model of pattern regeneration based on cell memory. PLoS ONE 10, e0118091 (2015)CrossRefGoogle Scholar
  38. 38.
    U. Yerushalmi, M. Teicher, Evolving synaptic plasticity with an evolutionary cellular development model. PLoS ONE 3, e3697 (2008)CrossRefGoogle Scholar
  39. 39.
    K.O. Stanley, Compositional pattern producing networks: A novel abstraction of development. Genet. Program Evolvable Mach. 8, 131–162 (2007)CrossRefGoogle Scholar
  40. 40.
    M. a. N. N. T. R., Natick (The MathWorks, Inc., Massachusetts, United States, 2012)Google Scholar
  41. 41.
    R. Hecht-Nielsen, Theory of the backpropagation neural network, in Neural Networks, 1989. IJCNN., International Joint Conference on, vil. 1 (1989), pp. 593–605Google Scholar
  42. 42.
    D. Marquardt, An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963)MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in IEEE International Conference on Neural Networks, pp. 586–591Google Scholar
  44. 44.
    N.J. Oviedo, P.A. Newmark, A. Sánchez Alvarado, Allometric scaling and proportion regulation in the freshwater planarian Schmidtea mediterranea. Dev. Dyn. 226, 326–333 (2003)Google Scholar
  45. 45.
    G. Deco, E.T. Rolls, L. Albantakis, R. Romo, Brain mechanisms for perceptual and reward-related decision-making. Prog. Neurobiol. 103, 194–213 (2013)CrossRefGoogle Scholar
  46. 46.
    K.D. Birnbaum, A.S. Alvarado, Slicing across kingdoms: regeneration in plants and animals. Cell 132, 697–710 (2008)CrossRefGoogle Scholar
  47. 47.
    D. Lobo, M. Solano, G.A. Bubenik, M. Levin, A linear-encoding model explains the variability of the target morphology in regeneration. J. R. Soc., Interface/R. Soc. 11, 20130918 (2014)CrossRefGoogle Scholar
  48. 48.
    L.N. Vandenberg, D.S. Adams, M. Levin, Normalized shape and location of perturbed craniofacial structures in the Xenopus tadpole reveal an innate ability to achieve correct morphology. Dev. Dyn. 241, 863–878 (2012)CrossRefGoogle Scholar
  49. 49.
    J. Mustard, M. Levin, Bioelectrical mechanisms for programming growth and form: taming physiological networks for soft body robotics, Soft Rob. 1, 169–191 (2014)Google Scholar
  50. 50.
    A. Tseng, M. Levin, Cracking the bioelectric code: Probing endogenous ionic controls of pattern formation. Commun. Integr. Biol. 6, 1–8 (2013)CrossRefGoogle Scholar
  51. 51.
    M. Levin, C.G. Stevenson, Regulation of cell behavior and tissue patterning by bioelectrical signals: challenges and opportunities for biomedical engineering. Annu. Rev. Biomed. Eng. 14, 295–323 (2012)CrossRefGoogle Scholar
  52. 52.
    M. Levin, Molecular bioelectricity: how endogenous voltage potentials control cell behavior and instruct pattern regulation in vivo. Mol. Biol. Cell 25, 3835–3850 (2014)CrossRefGoogle Scholar
  53. 53.
    M. Levin, Reprogramming cells and tissue patterning via bioelectrical pathways: molecular mechanisms and biomedical opportunities. Wiley Interdisc. Rev.: Syst. Biol. Med. 5, 657–676 (2013)Google Scholar
  54. 54.
    M. Levin, Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning. Bio Syst. 109, 243–261 (2012)Google Scholar
  55. 55.
    M. Levin, Endogenous bioelectrical networks store non-genetic patterning information during development and regeneration. J. Physiol. 592, 2295–2305 (2014)Google Scholar
  56. 56.
    F. Keijzer, M. van Duijn, P. Lyon, What nervous systems do: early evolution, input-output, and the skin brain thesis. Adapt. Behav. 21, 67–85 (2013)CrossRefGoogle Scholar
  57. 57.
    N.D. Holland, Early central nervous system evolution: an era of skin brains? Nat. Rev. Neurosci. 4, 617–627 (2003)CrossRefGoogle Scholar
  58. 58.
    G.A. Buznikov, Y.B. Shmukler, Possible role of “prenervous” neurotransmitters in cellular interactions of early embryogenesis: a hypothesis. Neurochem. Res. 6, 55–68 (1981)CrossRefGoogle Scholar
  59. 59.
    G. Buznikov, Y. Shmukler, J. Lauder, From oocyte to neuron: do neurotransmitters function in the same way throughout development? Cell. Mol. Neurobiol. 16, 537–559 (1996)CrossRefGoogle Scholar
  60. 60.
    H. Yan, L. Zhao, L. Hu, X. Wang, E. Wang, J. Wang, Nonequilibrium landscape theory of neural networks. Proc. Natl. Acad. Sci. 110, E4185–E4194 (2013)CrossRefGoogle Scholar
  61. 61.
    K. Friston, B. SenGupta, G. Auletta, Cognitive dynamics: From attractors to active inference. Proc. IEEE 102, 427–445 (2014)CrossRefGoogle Scholar
  62. 62.
    S. Bhattacharya, Q. Zhang, M. Andersen, A deterministic map of Waddington’s epigenetic landscape for cell fate specification. BMC Syst. Biol. 5, 85 (2011)CrossRefGoogle Scholar
  63. 63.
    B.D. MacArthur, A. Ma’ayan, I. Lemischka, Toward stem cell systems biology: from molecules to networks and landscapes. Cold Spring Harb. Symp.Quant. Biol. 2008, p. sqb. 2008.73. 061Google Scholar
  64. 64.
    S. Huang, The molecular and mathematical basis of Waddington’s epigenetic landscape: A framework for post-Darwinian biology? BioEssays 34, 149–157 (2012)CrossRefGoogle Scholar
  65. 65.
    E. Aboukhatwa, A. Aboobaker, An introduction to planarians and their stem cells,” in eLS, ed (John Wiley and Sons, Ltd, 2015)Google Scholar
  66. 66.
    D. Lobo, W.S. Beane, M. Levin, Modeling planarian regeneration: a primer for reverse-engineering the worm. PLoS Comput. Biol. 8, e1002481 (2012)CrossRefGoogle Scholar
  67. 67.
    E. Saló, K. Agata, Planarian regeneration: a classic topic claiming new attention. Int. J. Dev. Biol. 56, 1–4 (2012)CrossRefGoogle Scholar
  68. 68.
    P.W. Reddien, A. Sanchez Alvarado, Fundamentals of planarian regeneration. Annu. Rev. Cell Dev. Biol. 20, 725–57 (2004)Google Scholar
  69. 69.
    N.J. Oviedo, J. Morokuma, P. Walentek, I. Kema, M.B. Gu, J.-M. Ahn et al., Long-range neural and gap junction protein-mediated cues control polarity during planarian regeneration. Dev. Biol. 339, 188–199 (2010)CrossRefGoogle Scholar
  70. 70.
    T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jennifer Hammelman
    • 1
  • Daniel Lobo
    • 2
  • Michael Levin
    • 1
  1. 1.Biology Department, School of Arts and ScienceTufts UniversityMedfordUSA
  2. 2.Department of Biological SciencesUniversity of Maryland, Baltimore CountyBaltimoreUSA

Personalised recommendations