The influence of computational traits on the natural selection of the nervous system

Article

Abstract

This article addresses why the neural network model has been selected by nature against other computational models to generate behavior in complex multicellular clades. This question, which has not yet been addressed in research, should not be ignored because understanding this issue is necessary to have a complete picture of the evolutionary process of the nervous system. The starting point to discuss the issue is a proposal made 30 years ago: the free-moving hypothesis. This proposal establishes prediction as the main function of the brain and that all multicellular organisms that move require a brain in order to make predictions. This article contains a review contrasting this hypothesis with the discoveries made in the last 30 years within different biological kingdoms. Although none of these discoveries contradict the free-moving hypothesis, it still does not answer the main question. Alternative hypotheses about the origin of the nervous system are discussed in this paper, but they also are not able to answer the question. Six hypotheses are proposed as possible answers, and each of them is discussed by comparing neural processing systems with three other alternative processing systems. The result is that the neural processing system is selected against other kinds of processing systems because it has computational robustness to damage, allowing its function of prediction to be more durable. While this result, called the first neural processing principle, answers the initial question and permits a finished proof of the free-moving hypothesis, it gives rise to the question of how computationally robust a system must be to be selected by nature. This paper claims that the system selected must be computationally robust enough to have enough offspring to allow variation. This answer, named the second neural principle, determines the minimum amount of neurons that a neural processing system must have, but not the maximum. To address this issue, the third and fourth neural processing principles are stated, which determine that the maximum number of neurons is limited by energetic restrictions and body size, respectively. The results presented in this paper show that computational robustness is an important parameter to understand the evolution of nervous system.

Keywords

Evolution Nervous system Neural networks Computational robustness 

References

  1. Acar M, Mettetal J, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat Genet 40(4):471–475CrossRefGoogle Scholar
  2. Agerwala T (1974) Communication with automata. Hopkins computer research report. John Hopkins University, BaltimoreGoogle Scholar
  3. Alstott J et al (2009) Modeling the impact of lesions in the human brain. PLoS Comput Biol 5(6):e1000,408CrossRefGoogle Scholar
  4. Ames-III A (2000) CNS energy metabolism as related to function. Brain Res Rev 34(12):42–68CrossRefGoogle Scholar
  5. Armitage J, Holland I, Jenal U, Kenny B (2005) Neural networks in bacteria: making connections. J Bacteriol 187(1):26–36CrossRefGoogle Scholar
  6. Avlund M, Dodd IB, Semsey S, Sneppen K, Krishna S (2009) Why do phage play dice? J Virol 83(22):11416–11420CrossRefGoogle Scholar
  7. Balaban N et al (2004) Bacterial persistence as a phenotypic switch. Science 305(5690):1622–1625CrossRefGoogle Scholar
  8. Baluka F et al (2009) The root-brain hypothesis of charles and francis darwin. Plant Signal Behav 4(12):1121–1127CrossRefGoogle Scholar
  9. Baluska F, Mancuso S (2013) Root apex transition zone as oscillatory zone. Front Plant Sci 4:354CrossRefGoogle Scholar
  10. Baluska F et al (2005) Plant synapses: actin-based domains for cell-to-cell communication. Trends Plant Sci 10(3):106–111CrossRefGoogle Scholar
  11. Baluska F et al (2008) Vesicular secretion of auxin. Plant Signal Behav 3(4):254–256CrossRefGoogle Scholar
  12. Bear MF, Connors BW, Paradiso MA (2007) Neuroscience: exploring the brain. Lippincott Williams & Wilkins, PhiladelphiaGoogle Scholar
  13. Bejan A (2006) Advanced engineering thermodynamics, 3rd edn. Wiley, HobokenGoogle Scholar
  14. Bejan A, Lorente S (2011) The constructal law and the evolution of design in nature. Phys Life Rev 8(3):209–240CrossRefGoogle Scholar
  15. Bellingham MC, Lim R, Walmsley B (1998) Developmental changes in epsc quantal size and quantal content at a central glutamatergic synapse in rat. J Physiol 511(3):861–869CrossRefGoogle Scholar
  16. Bertens L et al (2015) Modeling biological gradient formation: combining partial differential equations and petri nets. Nat Comput 15(4):665–675MathSciNetCrossRefGoogle Scholar
  17. Bindschaedler C et al (2011) Growing up with bilateral hippocampal atrophy: from childhood to teenage. Cortex 47(8):931–944CrossRefGoogle Scholar
  18. Boisseau RP, Vogel D, Dussutour A (2016) Habituation in non-neural organisms: evidence from slime moulds. In: Proceedings of the royal society of London B: biological sciences, 283(1829)Google Scholar
  19. Bond C (2013) Locomotion and contraction in an asconoid calcareous sponge. Invertebr Biol 132(4):283–290CrossRefGoogle Scholar
  20. Bouché N, Fromm H (2004) Gaba in plants: just a metabolite? Trends Plant Sci 9(3):110–115CrossRefGoogle Scholar
  21. Bradford MJ, Roff DA (1993) Bet hedging and the diapause strategies of the cricket Allonemobius fasciatus. Ecology 74(4):1129–1135CrossRefGoogle Scholar
  22. Branco T, Staras K (2009) The probability of neurotransmitter release: variability and feedback control at single synapses. Nat Rev Neurosci 10:373–383CrossRefGoogle Scholar
  23. Bray D (2003) Molecular networks: the top-down view. Science 301(5641):1864–1865CrossRefGoogle Scholar
  24. Brenner E (2006) Plant neurobiology: an integrated view of plant signaling. Trends Plant Sci 11(8):413419CrossRefGoogle Scholar
  25. Brodal P (2010) The central nervous system. Oxford University Press, OxfordGoogle Scholar
  26. Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 12:336–349Google Scholar
  27. Butterfield NJ (2015) The neoproterozoic. Curr Biol 25(19):R859–R863CrossRefGoogle Scholar
  28. Cardelli L, Zavattaro G (2008) On the computational power of biochemistry. In: Horimoto K, Regensburger G, Rosenkranz M, Yoshida H (eds) Algebraic biology, vol 5147. Springer, Berlin, pp 65–80CrossRefGoogle Scholar
  29. Cartwright P et al (2007) Exceptionally preserved jellyfishes from the middle cambrian. PLoS ONE 2(10):e1121CrossRefGoogle Scholar
  30. Chase R (2000) Structure and function in the cerebral ganglion. Microsc Res Tech 49(6):511–520CrossRefGoogle Scholar
  31. Cherniak C (1994) Component placement optimization in the brain. J Neurosci 14(4):2418–2427Google Scholar
  32. de Mairan J (1729) Observation botanique. Histoire de l’Academie royale des sciences, pp 35–36Google Scholar
  33. Delcomyn F (1999) Foundations of neurobiology. WH Freeman, New YorkGoogle Scholar
  34. Fernando C et al (2009) Molecular circuits for associative learning in single-celled organisms. J R Soc Interface 6(34):463–469CrossRefGoogle Scholar
  35. Fromm J, Lautner S (2007) Electrical signals and their physiological significance in plants. Plant Cell Environ 30(3):249–257CrossRefGoogle Scholar
  36. Fukuda M, Yamamoto T, Llins R (2001) The isochronic band hypothesis and climbing fibre regulation of motricity: an experimental study. Eur J Neurosci 13(2):315–326CrossRefGoogle Scholar
  37. Garm A, Ekstrm P, Boudes M, Nilsson DE (2006) Rhopalia are integrated parts of the central nervous system in box jellyfish. Cell Tissue Res 325(2):333–343CrossRefGoogle Scholar
  38. Ghosh A, Pal N, Pal S (1995) Modeling of component failure in neural networks for robustness evaluation: an application to object extraction. IEEE Trans Neural Netw 6(3):648–656CrossRefGoogle Scholar
  39. Glover J, Fritzsch B (2009) Encyclopedia of neuroscience, chap. Brains of primitive chordates. Springer, BerlinGoogle Scholar
  40. Green RM et al (2002) Circadian rhythms confer a higher level of fitness to arabidopsis plants. Plant Physiol 129(2):576–584MathSciNetCrossRefGoogle Scholar
  41. Grunfest H (1959) Evolution of nervous control from primitive organisms to man. chap. Evolution of conduction in the nervous system, pp 43–86Google Scholar
  42. Hanken J, Wake DB (1993) Miniaturization of body size: organismal consequences and evolutionary significance. Annu Rev Ecol Syst 24:501–519CrossRefGoogle Scholar
  43. Harmer SL (2009) The circadian system in higher plants. Annu Rev Plant Biol 60(1):357–377CrossRefGoogle Scholar
  44. Hedrich R, Salvador-Recatalá V, Dreyer I (2016) Electrical wiring and long-distance plant communication. Trends Plant Sci 21(5):376–387CrossRefGoogle Scholar
  45. Hennessey TM, Rucker WB, McDiarmid CG (1979) Classical conditioning in paramecia. Anim Learn Behav 7(4):417–423CrossRefGoogle Scholar
  46. Hjelmfelt A, Weinberger ED, Ross J (1991) Chemical implementation of neural networks and turing machines. Proc Natl Acad Sci 88(24):10983–10987MATHCrossRefGoogle Scholar
  47. Hofman MA (1983) Energy metabolism, brain size and longevity in mammals. Q Rev Biol 58(4):495–512CrossRefGoogle Scholar
  48. Holland ND (2003) Early central nervous system evolution: an era of skin brains? Nat Rev Neurosci 4(8):617–627CrossRefGoogle Scholar
  49. Huxley J (2010) Evolution: the modern synthesis. The definitve edition. MIT Press, CambridgeGoogle Scholar
  50. Ionescu M, Păun G, Yokomori T (2006) Spiking neural p systems. Fundam Inf 71(2,3):279–308MathSciNetMATHGoogle Scholar
  51. Kalampokis A et al (2003) Robustness in biological neural networks. Phys A 317(34):581–590MATHCrossRefGoogle Scholar
  52. Katzenberger RJ et al (2013) A drosophila model of closed head traumatic brain injury. Proc Natl Acad Sci 110(44):E4152–E4159CrossRefGoogle Scholar
  53. Kazantsev VB et al (2004) Self-referential phase reset based on inferior olive oscillator dynamics. Proc Natl Acad Sci 101(52):18183–18188CrossRefGoogle Scholar
  54. Keijzer F, van Duijn M, Lyon P (2013) What nervous systems do: early evolution, inputoutput, and the skin brain thesis. Adapt Behav 21(2):67–85CrossRefGoogle Scholar
  55. Kinnersley AM, Turano FJ (2000) Gamma aminobutyric acid (gaba) and plant responses to stress. Crit Rev Plant Sci 19(6):479–509CrossRefGoogle Scholar
  56. Knoll A (2011) The multiple origins of complex multicellularity. Annu Rev Earth Planet Sci 39:217–239CrossRefGoogle Scholar
  57. Larkum ME, Zhu JJ, Sakmann B (1999) A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398:338–341CrossRefGoogle Scholar
  58. Laughlin SB, Sejnowski TJ (2003) Communication in neural networks. Science 301(5641):18701874CrossRefGoogle Scholar
  59. Lenton TM et al (2014) Co-evolution of eukaryotes and ocean oxygenation in the neoproterozoic era. Nat Geosci 7:257–265CrossRefGoogle Scholar
  60. Levy WB, Baxter RA (2002) Energy-efficient neuronal computation via quantal synaptic failures. J Neurosci 22(11):4746–4755Google Scholar
  61. Leys S, Mackie G (1997) Electrical recording from a glass sponge. Nature 387:29–30CrossRefGoogle Scholar
  62. Leys SP (2015) Elements of a ‘nervous system’ in sponges. J Exp Biol 218(4):581–591CrossRefGoogle Scholar
  63. Llinás R (1987) Mindwaves. chap. “Mindness” as a functional state of the brain. Oxford, pp 339–358Google Scholar
  64. Llinás R (2001) I of the vortex: from neurons to self. MIT press, CambridgeGoogle Scholar
  65. Lm A (1994) Molecular computation of solutions to combinatorial problems. Science 266(5187):1021–1024CrossRefGoogle Scholar
  66. Lyon P (2013) Developing scaffolds in evoution, culture and cognition. chap. Stress in mind: a stress response hypothesis cognitive cognition. MIT Press, pp 171–190Google Scholar
  67. Mackie G (2004) Central neural circuitry in the jellyfish Aglantha. Neurosignals 13(1–2):5–19CrossRefGoogle Scholar
  68. Mackie GO (1970) Neuroid conduction and the evolution of conducting tissues. The quarterly review of biology 45(4):319–332CrossRefGoogle Scholar
  69. Magnasco MO (1997) Chemical kinetics is turing universal. Phys Rev Lett 78(6):1190–1193CrossRefGoogle Scholar
  70. Makarenko V, Llins R (1998) Experimentally determined chaotic phase synchronization in a neuronal system. Proc Natl Acad Sci 95(26):15747–15752CrossRefGoogle Scholar
  71. Masi E et al (2009) Spatiotemporal dynamics of the electrical network activity in the root apex. Proc Natl Acad Sci 106(10):4048–4053CrossRefGoogle Scholar
  72. Mayr E (1997) The objects of selection. Proc Natl Acad Sci 94(6):2091–2094CrossRefGoogle Scholar
  73. McClung CR (2006) Plant circadian rhythms. Plant Cell 18(4):792–803CrossRefGoogle Scholar
  74. McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133MathSciNetMATHCrossRefGoogle Scholar
  75. Miguel-Tomé S (2015) Trajectories-state: A new neural mechanism to interpretate cerebral dynamics. In: Artificial computation in biology and medicine, Lecture notes in computer science, vol 9107. Springer International Publishing, pp 88–97Google Scholar
  76. Mitchell A et al (2009) Adaptive prediction of environmental changes by microorganisms. Nature 460:220–224CrossRefGoogle Scholar
  77. Mitchell-Olds T, Willis JH, Goldstein DB (2007) Which evolutionary processes influence natural genetic variation for phenotypic traits? Nat Rev Genet 8:845–856CrossRefGoogle Scholar
  78. Monk T (2014) The evolutionary origin of nervous systems and implications for neural computation. Ph.D. thesis, University of OtagoGoogle Scholar
  79. Monk T, Paulin MG (2014) Predation and the origin of neurones. Brain Behav Evol 84:246–261CrossRefGoogle Scholar
  80. Monk T, Paulin MG, Green P (2015) Ecological constraints on the origin of neurones. J Math Biol 71(6):1299–1324MathSciNetMATHCrossRefGoogle Scholar
  81. Moreno H et al (2009) Synaptic transmission block by presynaptic injection of oligomeric amyloid beta. Proc Natl Acad Sci 106(14):5901–5906CrossRefGoogle Scholar
  82. Moroz L (2009) On the independent origins of complex brains and neurons. Brain Behav Evol 74:177–190CrossRefGoogle Scholar
  83. Moroz LL, Kohn AB (2015) Unbiased view of synaptic and neuronal gene complement in ctenophores: are there pan-neuronal and pan-synaptic genes across metazoa? Integr Comp Biol 55(6):1028–1049Google Scholar
  84. Moroz L, Kohn A (2016) Independent origins of neurons and synapses: insights from ctenophores. In: Philosophical transactions of the royal society of London B: biological sciences 371(1685):1–14CrossRefGoogle Scholar
  85. Navlakha S, Barth A, Bar-Joseph Z (2015) Decreasing-rate pruning optimizes the construction of efficient and robust distributed networks. PLoS Comput Biol 11(7):1–23CrossRefGoogle Scholar
  86. Nickel M (2010) Evolutionary emergence of synaptic nervous systems: what can we learn from the non-synaptic, nerveless porifera? Invertebr Biol 129(1):1–16MathSciNetCrossRefGoogle Scholar
  87. Nickel M et al (2011) The contractile sponge epithelium sensu lato–body contraction of the demosponge tethya wilhelma is mediated by the pinacoderm. J Exp Biol 214(10):1692–1698CrossRefGoogle Scholar
  88. Niklas KJ, Newman SA (2013) The origins of multicellular organisms. Evol Dev 15(1):41–52CrossRefGoogle Scholar
  89. Nilsson DE, Gisln L, Coates MM, Skogh C, Garm A (2005) Advanced optics in a jellyfish eye. Nature 435:201–205CrossRefGoogle Scholar
  90. Niven JE, Laughlin SB (2008) Energy limitation as a selective pressure on the evolution of sensory systems. J Exp Biol 211(11):1792–1804CrossRefGoogle Scholar
  91. Okubo F, Yokomori T (2016) The computational capability of chemical reaction automata. Nat Comput 15(2):215–224MathSciNetMATHCrossRefGoogle Scholar
  92. Oliveira AG et al (2015) Circadian control sheds light on fungal bioluminescence. Curr Biol 25(7):964–968CrossRefGoogle Scholar
  93. Oyarce P, Gurovich L (2011) Evidence for the transmission of information through electric potentials in injured avocado trees. J Plant Physiol 168(2):103–108CrossRefGoogle Scholar
  94. Pantin C (1956) The origin of the nervous system. Publicazioni della Stazione Zoologica di Napoli 28:171–181Google Scholar
  95. Parker G (1919) Primitive nervous systems. Lippincott, New YorkGoogle Scholar
  96. Passano LM (1963) Primitive nervous systems. PNAS 50(2):306–313CrossRefGoogle Scholar
  97. Paulsen O, Heggelund P (1996) Quantal properties of spontaneous epscs in neurones of the guinea-pig dorsal lateral geniculate nucleus. J Physiol 496(3):759–772CrossRefGoogle Scholar
  98. Păun G (2002) Membrane computing: an introduction. Springer, BerlinMATHCrossRefGoogle Scholar
  99. Petri CA (1966) Communication with automata. Tech. Rep. RADC-TR-65–377. Griffiss Air Force Base, New YorkGoogle Scholar
  100. Polilov A (2008) Anatomy of the smallest of the coleoptera, feather-winged beetles from tribe nanosellini (coleoptera, ptiliidae) and limits to insect miniaturization. Entomol Rev 88:2633CrossRefGoogle Scholar
  101. Polilov AA (2012) The smallest insects evolve anucleate neurons. Arthropod Struct Dev 41(1):29–34Google Scholar
  102. Qian L, Soloveichik D, Winfree E (2011) Efficient turing-universal computation with DNA polymers. In: DNA computing and molecular programming, vol 6518. Springer International Publishing, pp 123–140Google Scholar
  103. Ramesh SA et al (2015) Gaba signalling modulates plant growth by directly regulating the activity of plant-specific anion transporters. Nat Commun 6(7879):1–9Google Scholar
  104. Renard E et al (2009) Origin of the neuro-sensory system: new and expected insights from sponges. Integr Zool 4(3):294–308CrossRefGoogle Scholar
  105. Roberts A (2007) Plasmodesmal Structure and Development. Plasmodesmata, pp. 1–32Google Scholar
  106. Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386–408CrossRefGoogle Scholar
  107. Satterlie R (2002) Control of swimming in jellyfish: a comparative story. Can J Zool 80:1654–1669CrossRefGoogle Scholar
  108. Satterlie RA (2011) Do jellyfish have central nervous systems? J Exp Biol 214(8):1215–1223CrossRefGoogle Scholar
  109. Schmidt-Nielsen K (1984) Scaling: why is animal size so important?. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  110. Scialdone A, Howard M (2015) How plants manage food reserves at night: quantitative models and open questions. Front Plant Sci 6(204):1–7Google Scholar
  111. Scialdone A et al (2013) Arabidopsis plants perform arithmetic division to prevent starvation at night. Elife 2:e00669. doi:10.7554/eLife.00669 CrossRefGoogle Scholar
  112. Sibaoka T (1991) Rapid plant movements triggered by action potentials. Bot Mag 104(1):73–95CrossRefGoogle Scholar
  113. Siegelmann HT (1998) Neural networks and analog computation: beyond the turing limit (progress in theoretical computer science). Birkhäuser, BostonGoogle Scholar
  114. Smith C, Pivovarova N, Reese T (2015) Coordinated feeding behavior in trichoplax, an animal without synapses. PLoS ONE 10(9):e0136,098CrossRefGoogle Scholar
  115. Smith C et al (2014) Novel cell types, neurosecretory cells, and body plan of the early-diverging metazoan trichoplax adhaerens. Curr Biol 24(14):1565–1572CrossRefGoogle Scholar
  116. Soloveichik D, Seelig G, Winfree E (2010) DNA as a universal substrate for chemical kinetics. Proc Natl Acad Sci 107(12):5393–5398CrossRefGoogle Scholar
  117. Sukhov V, Nerush V, Orlova L, Vodeneev V (2011) Simulation of action potential propagation in plants. J Theor Biol 291:47–55CrossRefGoogle Scholar
  118. Tagkopoulos I, Liu YC, Tavazoie S (2008) Predictive behavior within microbial genetic networks. Science 320:1313–1317CrossRefGoogle Scholar
  119. Thome C et al (2014) Axon-carrying dendrites convey privileged synaptic input in hippocampal neurons. Neuron 83(6):1418–1430CrossRefGoogle Scholar
  120. Volkov A, Foster J, Markin V (2010) Signal transduction in mimosa pudica: biologically closed electrical circuits. Plant Cell Environ 33(5):816–827Google Scholar
  121. Volkov AG, Markin VS (2015) Active and passive electrical signaling in plants. Prog Bot 76:143–176CrossRefGoogle Scholar
  122. Wall J, Xu J, Wang X (2002) Human brain plasticity: an emerging view of the multiple substrates and mechanisms that cause cortical changes and related sensory dysfunctions after injuries of sensory inputs from the body. Brain Res Rev 39(23):181–215CrossRefGoogle Scholar
  123. Wehner R (2005) Sensory physiology: brainless eyes. Nature 435(7039):157–159CrossRefGoogle Scholar
  124. Yan X et al (2009) Research progress on electrical signals in higher plants. Prog Nat Sci 19(5):531–541CrossRefGoogle Scholar
  125. Yang R, Lenaghan SC, Zhang M, Xia L (2010) A mathematical model on the closing and opening mechanism for venus flytrap. Plant Signal Behav 5(8):968–978CrossRefGoogle Scholar
  126. Yokawa K et al (2014) Binary decisions in maize root behavior: Y-maze system as tool for unconventional computation in plants. Plant Signal Behav 10(5–6):381–390Google Scholar
  127. Zhao D et al (2015) High-resolution non-contact measurement of the electrical activity of plants in situ using optical recording. Sci Rep 5:13,425CrossRefGoogle Scholar
  128. Zylberberg A et al (2011) The human turing machine: a neural framework for mental programs. Trends Cognit Sci 15(7):293–300Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Grupo de Investigación en Minería de Datos (MiDa)Universidad de SalamancaSalamancaSpain

Personalised recommendations