Top-down models in biology: Explanation and control of complex living systems above the molecular level

Journal of the Royal Society Interface

Volumn 13, Issue 124, 2016, Pages

  Pezzulo, Giovanni ^b   Levin, Michael ^a  

^a TUFTS UNIVERSITY   (United States)

^b INSTITUTE OF COGNITIVE SCIENCES AND TECHNOLOGIES   (Italy)

Author keywords

Cognitive modelling; Developmental biology; Integrative; Regeneration; Remodelling; Top down

Indexed keywords

BIOLOGY; NEUROLOGY;

COGNITIVE MODELLING; DEVELOPMENTAL BIOLOGY; INTEGRATIVE; REGENERATION; REMODELLING; TOPDOWN;

BIOLOGICAL SYSTEMS;

BIOENGINEERING; COGNITIVE NEUROSCIENCE; DEVELOPMENTAL BIOLOGY; EMBRYO DEVELOPMENT; HOMEOSTASIS; MODEL; MORPHOGENESIS; PHYSICS; REGENERATION; REGENERATIVE MEDICINE; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; HUMAN;

ANIMALS; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, BIOLOGICAL;

EID: 85006137985     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2016.0555     Document Type: Article

References (151)

1. Ross J, Arkin AP. 2009 Complex systems: from chemistry to systems biology. Proc. Natl Acad. Sci. USA 106, 6433-6434. (doi:10.1073/pnas. 0903406106)
2. Farinella-Ferruzza N. 1956 The transformation of a tail into limb after xenoplastic transplantation. Experientia 12, 304-305. (doi:10.1007/ BF02159624)
3. Bubenik AB, Pavlansky R. 1965 Trophic responses to trauma in growing antlers. J. Exp. Zool. 159, 289-302. (doi:10.1002/jez.1401590302)
4. Levin M. 2012 Morphogenetic fields in embryogenesis, regeneration, and cancer: non-local control of complex patterning. Biosystems 109, 243-261. (doi:10.1016/j.biosystems. 2012.04.005)
5. Mustard J, Levin M. 2014 Bioelectrical mechanisms for programming growth and form: taming physiological networks for soft body robotics. Soft Robot. 1, 169-191. (doi:10.1089/soro. 2014.0011)
6. Lobo D, Solano M, Bubenik GA, Levin M. 2014 A linear-encoding model explains the variability of the target morphology in regeneration. J. R. Soc. Interface 11, 20130918. (doi:10.1098/rsif. 2013.0918)
7. Munjal A, Philippe J-M, Munro E, Lecuit T. 2015 A self-organized biomechanical network drives shape changes during tissue morphogenesis. Nature 524, 351-355. (doi:10.1038/nature14603)
8. Beloussov LV. 2001 Morphogenetic fields: outlining the alternatives and enlarging the context. Rivista Di Biologia-Biol. Forum 94, 219-235.
9. Noble D. 2012 A theory of biological relativity: no privileged level of causation. Interface Focus 2, 55-64. (doi:10.1098/rsfs.2011.0067)
10. Shadmehr R, Smith MA, Krakauer JW. 2010 Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89-108. (doi:10.1146/annurev-neuro-060909-153135)
11. Ashby WR. 1952 Design for a brain. Oxford, UK: Wiley.
12. Powers WT. 1973 Behavior: the control of perception. Hawthorne, NY: Aldine.
13. Wiener N. 1948 Cybernetics: or control and communication in the animal and the machine. Cambridge, MA: The MIT Press.
14. Pezzulo G, Cisek P. 2016 Navigating the affordance landscape: feedback control as a process model of behavior and cognition. Trends Cogn. Sci. 20, 414-424. (doi:10.1016/j.tics.2016.03.013)
15. Todorov E. 2004 Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907-915. (doi:10.1038/nn1309)
16. Kording KP, Wolpert DM. 2006 Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. 10, 319-326. (doi:10.1016/j.tics.2006.05.003)
17. Friston K. 2010 The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127-138. (doi:10.1038/nrn2787)
18. Jeannerod M. 2006 Motor cognition. Oxford, UK: Oxford University Press.
19. Olshausen BA, Field DJ. 1996 Emergence of simplecell receptive field properties by learning a sparse code for natural images. Nature 381, 607-609. (doi:10.1038/381607a0)
20. Pouget A, Beck JM, Ma WJ, Latham PE. 2013 Probabilistic brains: knowns and unknowns. Nat. Neurosci. 16, 1170-1178. (doi:10.1038/nn.3495)
21. Schultz W, Dayan P, Montague PR. 1997 A neural substrate of prediction and reward. Science 275, 1593-1599. (doi:10.1126/science.275.5306.1593)
22. Anderson JR. 1983 The architecture of cognition. Cambridge, MA: Harvard University Press.
23. Feynman R. 1942 The principle of least action in quantum mechanics. PhD thesis, Princeton University, Princeton, NJ.
24. Ogborn J, Hanc J, Taylor EF. 2006 Action on stage: historical introduction. In Presented at the GIREP Conf.
25. Darwin C. 1859 On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. London, UK: John Murray.
26. Nagel E. 1979 Teleology revisited and other essays in the philosophy and history of science. New York, NY: Columbia University Press.
27. Rosenblueth A, Wiener N, Bigelow J. 1943 Behavior, purpose and teleology. Philos. Sci. 10, 18-24. (doi:10.1086/286788)
28. Ruse M. 1989 Teleology in biology: is it a cause for concern? Trends Ecol. Evol. 4, 51-54. (doi:10.1016/ 0169-5347(89)90143-2)
29. Pezzulo G, Verschure PF, Balkenius C, Pennartz CM. 2014 The principles of goal-directed decisionmaking: from neural mechanisms to computation and robotics. Phil. Trans. R. Soc. B 369, 20130470. (doi:10.1098/rstb.2013.0470)
30. Pezzulo G, Castelfranchi C. 2009 Thinking as the control of imagination: a conceptual framework for goal-directed systems. Psychol. Res. 73, 559-577. (doi:10.1007/s00426-009-0237-z)
31. Pezzulo G, Rigoli F, Friston KJ. 2015 Active inference, homeostatic regulation and adaptive behavioural control. Prog. Neurobiol. 134, 17-35. (doi:10.1016/j.pneurobio.2015.09.001)
32. Auletta G, Ellis GF, Jaeger L. 2008 Top-down causation by information control: from a philosophical problem to a scientific research programme. J. R. Soc. Interface 5, 1159-1172. (doi:10.1098/rsif.2008.0018)
33. Ellis GF. 2009 Top-down causation and the human brain. In Downward causation and the neurobiology of free will, pp. 63-81. Berlin, Germany: Springer.
34. Ellis GFR. 2008 On the nature of causation in complex systems. Trans. R. Soc. South Africa 63, 69-84. (doi:10.1080/00359190809519211)
35. Ellis GFR, Noble D, O'Connor T. 2012 Top-down causation: an integrating theme within and across the sciences? Introduction. Interface Focus 2, 1-3. (doi:10.1098/rsfs.2011.0110)
36. Jaeger L, Calkins ER. 2012 Downward causation by information control in micro-organisms. Interface Focus 2, 26-41. (doi:10.1098/rsfs.2011. 0045)
37. Juarrero A. 2009 Top-down causation and autonomy in complex systems. In Downward causation and the neurobiology of free will, pp. 83-102. Berlin, Germany: Springer.
38. Noble D. 2008 Genes and causation. Phil. Trans. R. Soc. A 366, 3001-3015. (doi:10.1098/rsta. 2008.0086)
39. Soto AM, Sonnenschein C, Miquel PA. 2008 On physicalism and downward causation in developmental and cancer biology. Acta Biotheor. 56, 257-274. (doi:10.1007/s10441-008-9052-y)
40. Auletta G, Colage I, Jeannerod M. 2013 Brains top down: is top-down causation challenging neuroscience? Singapore: World Scientific.
41. Hoel EP, Albantakis L, Tononi G. 2013 Quantifying causal emergence shows that macro can beat micro. Proc. Natl Acad. Sci. USA 110, 19 790-19 795. (doi:10.1073/pnas.1314922110)
42. Kravchenko-Balasha N, Shin YS, Sutherland A, Levine RD, Heath JR. 2016 Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement. Proc. Natl Acad. Sci. USA 113, 5520-5525. (doi:10.1073/pnas. 1602171113)
43. Dennett D. 1987 The intentional stance. Cambridge, MA: MIT Press.
44. Mäkelä T, Annila A. 2010 Natural patterns of energy dispersal. Phys. Life Rev. 7, 477-498. (doi:10.1016/ j.plrev.2010.10.001)
45. Popper KR. 1963 Conjectures and refutations. London, UK: Routledge and Kegan Paul.
46. Kersten D, Yuille A. 2003 Bayesian models of object perception. Curr. Opin. Neurobiol. 13, 150-158. (doi:10.1016/S0959-4388(03)00042-4)
47. Hinton GE. 2007 Learning multiple layers of representation. Trends Cogn. Sci. 11, 428-434. (doi:10.1016/j.tics.2007.09.004)
48. Gershman SJ, Blei DM. 2012 A tutorial on Bayesian nonparametric models. J. Math. Psychol. 56, 1-12. (doi:10.1016/j.jmp.2011.08.004)
49. Stoianov I, Genovesio A, Pezzulo G. 2015 Prefrontal goal codes emerge as latent states in probabilistic value learning. J. Cogn. Neurosci. 28, 140-157. (doi:10.1162/jocn-a-00886)
50. Feynman R. 1967 The character of physical law. Cambridge, MA: MIT Press. See https://mitpress.mit. edu/books/character-physical-law (accessed 8 April 2016).
51. Zipf GK. 1949 Human behavior and the principle of least effort: an introduction to human ecology. Boston, MA: Addison-Wesley Press.
52. Friston K, Levin M, Sengupta B, Pezzulo G. 2015 Knowing one's place: a free-energy approach to pattern regulation. J. R. Soc. Interface 12, 20141383. (doi:10.1098/rsif.2014.1383)
53. Pezzulo G, Levin M. 2015 Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs. Integr. Biol. 7, 1487-1517. (doi:10.1039/C5IB00221D)
54. Ellis GFR. 2012 Top-down causation and emergence: some comments on mechanisms. Interface Focus 2, 126-140. (doi:10.1098/rsfs.2011.0062)
55. Okasha S. 2012 Emergence, hierarchy and top-down causation in evolutionary biology. Interface Focus 2, 49-54. (doi:10.1098/rsfs.2011.0046)
56. Pernu TK, Annila A. 2012 Natural emergence. Complexity 17, 44-47. (doi:10.1002/cplx.21388)
57. Anderson PW. 1972 More is different. Broken symmetry and nature of hierarchical structure of science. Science 177, 393. (doi:10.1126/science. 177.4047.393)
58. Gu M, Weedbrook C, Perales A, Nielsen MA. 2009 More really is different. Physica D 238, 835-839. (doi:10.1016/j.physd.2008.12.016)
59. Haken H. 1983 Advanced synergetics: instability hierarchies of self-organizing systems and devices. Berlin, Germany: Springer.
60. Haken H, Wunderlin A, Yigitbasi S. 1995 An introduction to synergetics. Open Syst. Inf. Dyn. 3, 97-130. (doi:10.1007/BF02228811)
61. Dewan EM. 1976 Consciousness as an emergent causal agent in the context of control system theory. In Consciousness and the brain: a scientific and philosophical inquiry (ed. GMG Globus), pp. 181-198. New York, NY: Plenum Press.
62. Sperry RW. 1969 A modified concept of consciousness. Psychol. Rev. 76, 532-536. (doi:10. 1037/h0028156)
63. Landau LD, Ginzburg VL. 1950 On the theory of superconductivity. Zh. Eksp. Teor. Fiz. 20, 1064.
64. Bardeen J, Cooper LN, Schrieffer JR. 1957 Microscopic theory of superconductivity. Phys. Rev. 106, 162-164. (doi:10.1103/PhysRev.106.162)
65. Hopfield JJ. 1982 Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554-2558. (doi:10.1073/pnas.79.8.2554)
66. Davies PC, Demetrius L, Tuszynski JA. 2011 Cancer as a dynamical phase transition. Theor. Biol. Med. Model. 8, 30. (doi:10.1186/1742-4682-8-30)
67. Huang S, Ernberg I, Kauffman S. 2009 Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin. Cell Dev. Biol. 20, 869-876. (doi:10.1016/j. semcdb.2009.07.003)
68. Sole RV, Macia J. 2013 Expanding the landscape of biological computation with synthetic multicellular consortia. Nat. Comput. 12, 485-497. (doi:10.1007/ s11047-013-9380-y)
69. Friston K, Daunizeau J, Kilner J, Kiebel SJ. 2010 Action and behavior: a free-energy formulation. Biol. Cybern. 102, 227-260. (doi:10.1007/s00422-010-0364-z)
70. Balduzzi D, Tononi G. 2008 Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput. Biol. 4, e1000091. (doi:10.1371/journal.pcbi.1000091)
71. Edlund JA, Chaumont N, Hintze A, Koch C, Tononi G, Adami C. 2011 Integrated information increases with fitness in the evolution of animats. PLoS Comput. Biol. 7, e1002236. (doi:10.1371/journal. pcbi.1002236)
72. Klyubin AS, Polani D, Nehaniv CL. 2005 Empowerment: a universal agent-centric measure of control. IEEE C Evol. Computat. 1, 128-135.
73. Martius G, Herrmann JM, Der R. 2007 Guided selforganisation for autonomous robot development. In Advances in artificial life, vol. 4648 (eds FAlmeida E Costa, L Rocha, E Costa, I Harvey, A Coutinho), pp. 766-775. Berlin, Germany: Springer.
74. Friston K. 2008 Hierarchical models in the brain. PLoS Comput. Biol. 4, e1000211. (doi:10.1371/ journal.pcbi.1000211)
75. Friston K, Schwartenbeck P, FitzGerald T, Moutoussis M, Behrens T, Dolan RJ. 2014 The anatomy of choice: dopamine and decision-making. Phil. Trans. R. Soc. B 369, 20130481. (doi:10.1098/rstb. 2013.0481)
76. Friston K, Rigoli F, Ognibene D, Mathys C, Fitzgerald T, Pezzulo G. 2015 Active inference and epistemic value. Cogn. Neurosci. 6, 187-214. (doi:10.1080/ 17588928.2015.1020053)
77. Adams RA, Stephan KE, Brown HR, Frith CD, Friston KJ. 2013 The computational anatomy of psychosis. Front. Psychiatry 4, 47. (doi:10.3389/fpsyt.2013. 00047)
78. Friston K. 2013 Life as we know it. J. R. Soc. Interface 10, 20130475. (doi:10.1098/rsif.2013.0475)
79. Kiebel SJ, Daunizeau J, Friston KJ. 2008 A hierarchy of time-scales and the brain. PLoS Comput. Biol. 4, e1000209. (doi:10.1371/journal.pcbi. 1000209)
80. Pezzulo G. 2014 Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference. Cogn. Affect. Behav. Neurosci. 14, 902-911. (doi:10.3758/s13415-013-0227-x)
81. Seth AK. 2013 Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci. 17, 565-573. (doi:10.1016/j.tics.2013.09.007)
82. von Helmholtz H. 1866 Concerning the perceptions in general. In Treatise on physiological optics, vol. 3 (ed. JPC Southall). New York, NY: Dover.
83. Friston K et al. 2012 Dopamine, affordance and active inference. PLoS Comput. Biol. 8, e1002327. (doi:10.1371/journal.pcbi.1002327)
84. Friston K, Adams RA, Perrinet L, Breakspear M. 2012 Perceptions as hypotheses: saccades as experiments. Front. Psychol. 3, 151. (doi:10.3389/fpsyg.2012.00151)
85. Pezzulo G, Cartoni E, Rigoli F, Pio-Lopez L, Friston K. 2016 Active inference, epistemic value, and vicarious trial and error. Learn. Mem. 23, 322-338. (doi:10.1101/lm.041780.116)
86. Friston K, FitzGerald T, Rigoli F, Schwartenbeck P, O'Doherty J, Pezzulo G. 2016 Active inference and learning. Neurosci. Biobehav. Rev. 68, 862-879. (doi:10.1016/j.neubiorev.2016.06.022)
87. Bastos AM, Usrey WM, Adams RA, Mangun GR, Fries P, Friston KJ. 2012 Canonical microcircuits for predictive coding. Neuron 76, 695-711. (doi:10. 1016/j.neuron.2012.10.038)
88. Maturana HR, Varela FJ. 1980 Autopoiesis and cognition: the realization of living. Dordrecht, Holland: D Reidel.
89. Rao RP, Ballard DH. 1999 Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79-87. (doi:10.1038/4580)
90. Lisman J. 2015 The challenge of understanding the brain: where we stand in 2015. Neuron 86, 864-882. (doi:10.1016/j.neuron.2015. 03.032)
91. Vandenberg LN, Adams DS, Levin M. 2012 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. (doi:10. 1002/dvdy.23770)
92. Brandts WA. 1993 A field model of left-right asymmetries in the pattern regulation of a cell. IMA J. Math. Appl. Med. Biol. 10, 31-50. (doi:10.1093/ imammb/10.1.31)
93. Brandts WAM, Trainor LEH. 1990 A nonlinear field model of pattern-formation: intercalation in morphalactic regulation. J. Theor. Biol. 146, 37-56. (doi:10.1016/S0022-5193(05)80043-8)
94. Brandts WAM, Trainor LEH. 1990 A nonlinear field model of pattern-formation: application to intracellular pattern reversal in Tetrahymena. J. Theor. Biol. 146, 57-85. (doi:10.1016/S0022-5193(05)80044-X)
95. Hironaka K, Morishita Y. 2012 Encoding and decoding of positional information in morphogendependent patterning. Curr. Opin. Genet. Dev. 22, 553-561. (doi:10.1016/j.gde. 2012.10.002)
96. Ogawa K, Miyake Y. 2011 Generation model of positional values as cell operation during the development of multicellular organisms. Biosystems 103, 400-409. (doi:10.1016/j.biosystems.2010. 12.002)
97. Rosen R. 1985 Anticipatory systems: philosophical, mathematical, and methodological foundations, 1st edn. Oxford, UK: Pergamon Press.
98. Gorgcki J, Gorgcka FN. 2007 Chemical wave based programming in reaction-diffusion systems. Int. J. Unconv. Comput. 3, 259-270.
99. Scarle S. 2009 Implications of the Turing completeness of reaction-diffusion models, informed by GPGPU simulations on an XBox 360: cardiac arrhythmias, re-entry and the halting problem. Comput. Biol. Chem. 33, 253-260.
100. Schumann A, Adamatzky A. 2009 Toward semantical model of reaction-diffusion computing. Kybernetes 38, 1518-1531. (doi:10.1108/ 03684920910991504)
101. Ling H, Samarasinghe S, Kulasiri D. 2013 Novel recurrent neural network for modelling biological networks: oscillatory p53 interaction dynamics. Biosystems 114, 191-205. (doi:10.1016/j. biosystems.2013.08.004)
102. Watson RA, Buckley CL, Mills R, Davies A. 2010 Associative memory in gene regulation networks. In 12th Int. Conf. on the Synthesis and Simulation of Living Systems, Odense, Denmark, 19-23 August 2010, pp. 194-201. Cambridge, MA: MIT Press.
103. Craddock TJ, Tuszynski JA, Hameroff S. 2012 Cytoskeletal signaling: is memory encoded in microtubule lattices by CaMKII phosphorylation? PLoS Comput. Biol. 8, e1002421. (doi:10.1371/ journal.pcbi.1002421)
104. Craddock TJ, Beauchemin C, Tuszynski JA. 2009 Information processing mechanisms in microtubules at physiological temperature: model predictions for experimental tests. Biosystems 97, 28-34. (doi:10. 1016/j.biosystems.2009.04.001)
105. Hameroff SR, Craddock TJ, Tuszynski JA. 2010 'Memory bytes' - molecular match for CaMKII phosphorylation encoding of microtubule lattices. J. Integr. Neurosci. 9, 253-267. (doi:10.1142/ S0219635210002482)
106. Bates E. 2015 Ion channels in development and cancer. Annu. Rev. Cell Dev. Biol. 31, 231-247. (doi:10.1146/annurev-cellbio-100814-125338)
107. Adams DS. 2008 A new tool for tissue engineers: ions as regulators of morphogenesis during development and regeneration. Tissue Eng. Part A 14, 1461-1468. (doi:10.1089/ten.tea.2008.0080)
108. McCaig CD, Rajnicek AM, Song B, Zhao M. 2005 Controlling cell behavior electrically: current views and future potential. Physiol. Rev. 85, 943-978. (doi:10.1152/physrev.00020.2004)
109. Nuccitelli R. 2003 Endogenous electric fields in embryos during development, regeneration and wound healing. Radiat. Prot. Dosimetry 106, 375-383. (doi:10.1093/oxfordjournals.rpd. a006375)
110. Palacios-Prado N, Bukauskas FF. 2009 Heterotypic gap junction channels as voltage-sensitive valves for intercellular signaling. Proc. Natl Acad. Sci. USA 106, 14 855-14 860. (doi:10.1073/pnas. 0901923106)
111. Buznikov GA, Lambert HW, Lauder JM. 2001 Serotonin and serotonin-like substances as regulators of early embryogenesis and morphogenesis. Cell Tissue Res. 305, 177-186. (doi:10.1007/s004410100408)
112. Baluška F, Levin M. 2016 On having no head: cognition throughout biological systems. Front. Psychol. 7, 902.
113. Levin M. 2014 Endogenous bioelectrical networks store non-genetic patterning information during development and regeneration. J. Physiol. 592, 2295-2305. (doi:10.1113/jphysiol.2014.271940)
114. Sullivan KG, Emmons-Bell M, Levin M. 2016 Physiological inputs regulate species-specific anatomy during embryogenesis and regeneration. Commun. Integr Biol. 9, e1192733. (doi:10.1080/ 19420889.2016.1192733)
115. Levin M. 2014 Molecular bioelectricity: how endogenous voltage potentials control cell behavior and instruct pattern regulation in vivo. Mol. Biol. Cell 25, 3835-3850. (doi:10.1091/mbc.E13-12-0708)
116. Levin M. 2013 Reprogramming cells and tissue patterning via bioelectrical pathways: molecular mechanisms and biomedical opportunities. Wiley Interdiscip. Rev. Syst. Biol. Med. 5, 657-676. (doi:10.1002/wsbm.1236)
117. Nakajima K et al. 2015 KCNJ15/Kir4.2 couples with polyamines to sense weak extracellular electric fields in galvanotaxis. Nat. Commun. 6, 8532. (doi:10. 1038/ncomms9532)
118. Stoianov I, Zorzi M. 2012 Emergence of a 'visual number sense' in hierarchical generative models. Nat. Neurosci. 15, 194-196. (doi:10.1038/nn.2996)
119. Cox DD, Dean T. 2014 Neural networks and neuroscience-inspired computer vision. Curr. Biol. 24, R921-R929. (doi:10.1016/j.cub.2014.08.026)
120. Durant F, Lobo D, Hammelman J, Levin M. 2016 Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form. Regeneration 3, 78-102. (doi:10.1002/reg2.54)
121. Emmons-Bell M et al. 2015 Gap junctional blockade stochastically induces different species-specific head anatomies in genetically wild-type Girardia dorotocephala flatworms. Int. J. Mol. Sci. 16, 27 865-27 896. (doi:10.3390/ijms161126065)
122. Oviedo NJ, Morokuma J, Walentek P, Kema IP, Gu MB, Ahn JM, Hwang JS, Gojobori T, Levin M. 2010 Long-range neural and gap junction proteinmediated cues control polarity during planarian regeneration. Dev. Biol. 339, 188-199. (doi:10. 1016/j.ydbio.2009.12.012)
123. Donnarumma F, Prevete R, Trautteur G. 2012 Programming in the brain: a neural network theoretical framework. Connect. Sci. 24, 71-90. (doi:10.1080/09540091.2012.684670)
124. Buonomano DV, Maass W. 2009 State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113-125. (doi:10. 1038/nrn2558)
125. Donnarumma F, Maisto D, Pezzulo G. 2016 Problem solving as probabilistic inference with subgoaling: explaining human successes and pitfalls in the tower of Hanoi. PLoS Comput. Biol. 12, e1004864. (doi:10.1371/journal.pcbi.1004864)
126. Pai VP, Martyniuk CJ, Echeverri K, Sundelacruz S, Kaplan DL, Levin M. 2015 Genome-wide analysis reveals conserved transcriptional responses downstream of resting potential change in Xenopus embryos, axolotl regeneration, and human mesenchymal cell differentiation. Regeneration 3, 3-25. (doi:10.1002/reg2.48)
127. Tseng A-S, Levin M. 2012 Transducing bioelectric signals into epigenetic pathways during tadpole tail regeneration. Anatom. Rec. 295, 1541-1551. (doi:10.1002/ar.22495)
128. Tseng A, Levin M. 2013 Cracking the bioelectric code: probing endogenous ionic controls of pattern formation. Commun. Integr. Biol. 6, e22595. (doi:10. 4161/cib.22595)
129. Doursat R, Sayama H, Michel O. 2013 A review of morphogenetic engineering. Nat. Comput. 12, 517-535. (doi:10.1007/s11047-013-9398-1)
130. Levin M. 2011 The wisdom of the body: future techniques and approaches to morphogenetic fields in regenerative medicine, developmental biology and cancer. Regen. Med. 6, 667-673. (doi:10.2217/ rme.11.69)
131. Kim S, Kim HJ, Jeon NL. 2010 Biological applications of microfluidic gradient devices. Integr. Biol. (Camb.) 2, 584-603. (doi:10.1039/c0ib00055h)
132. Morishita Y, Hironaka K. 2013 Systems approach to developmental biology: designs for robust patterning. IET Syst. Biol. 7, 38-49. (doi:10.1049/ iet-syb.2012.0042)
133. Sheeba CJ, Andrade RP, Palmeirim I. 2014 Limb patterning: from signaling gradients to molecular oscillations. J. Mol. Biol. 426, 780-784. (doi:10. 1016/j.jmb.2013.11.022)
134. Slack J. 2014 Establishment of spatial pattern. Wiley Interdiscip. Rev. Dev. Biol. 3, 379-388. (doi:10. 1002/wdev.144)
135. Brouzes E, Farge E. 2004 Interplay of mechanical deformation and patterned gene expression in developing embryos. Curr. Opin. Genet. Dev. 14, 367-374. (doi:10.1016/j.gde.2004.06.005)
136. Mammoto A, Mammoto T, Ingber DE. 2012 Mechanosensitive mechanisms in transcriptional regulation. J. Cell Sci. 125, 3061-3073. (doi:10. 1242/jcs.093005)
137. von Dassow M, Davidson LA. 2011 Physics and the canalization of morphogenesis: a grand challenge in organismal biology. Phys. Biol. 8, 45002. (doi:10. 1088/1478-3975/8/4/045002)
138. Beloussov LV. 2013 Morphogenesis can be driven by properly parametrised mechanical feedback. Eur. Phys. J E Soft Matter 36, 132. (doi:10.1140/epje/ i2013-13132-x)
139. Chang F, Minc N. 2014 Electrochemical control of cell and tissue polarity. Annu. Rev. Cell Dev. Biol. 30, 317-336. (doi:10.1146/annurev-cellbio-100913-013357)
140. Stewart S, Rojas-Munoz A, Izpisua Belmonte JC. 2007 Bioelectricity and epimorphic regeneration. Bioessays 29, 1133-1137. (doi:10.1002/bies.20656)
141. Raspopovic J, Marcon L, Russo L, Sharpe J. 2014 Modeling digits. Digit patterning is controlled by a Bmp-Sox9-Wnt Turing network modulated by morphogen gradients. Science 345, 566-570. (doi:10.1126/science.1252960)
142. Papageorgiou S. 1984 A hierarchical polar coordinate model for epimorphic regeneration. J. Theor. Biol. 109, 533-554. (doi:10.1016/S0022-5193(84)80157-5)
143. Meinhardt H. 1983 A boundary model for pattern formation in vertebrate limbs. J. Embryol. Exp. Morphol. 76, 115-137.
144. French V. 1981 Pattern regulation and regeneration. Phil. Trans. R. Soc. Lond. B 295, 601-6171. (doi:10. 1098/rstb.1981.0163)
145. Nelsen EM, Frankel J. 1989 Maintenance and regulation of cellular handedness in Tetrahymena. Development 105, 457-471.
146. Verschure P, Pennartz CMA, Pezzulo G. 2014 The why, what, where, when and how of goal-directed choice: neuronal and computational principles. Phil. Trans. R. Soc. B 369, 20130483. (doi:10.1098/rstb. 2013.0483)
147. Skinner BF. 1938 The behavior of organisms: an experimental analysis. New York, NY: Appleton- Century-Crofts.
148. Tseng AS, Beane WS, Lemire JM, Masi A, Levin M. 2010 Induction of vertebrate regeneration by a transient sodium current. J. Neurosci. 30, 13 192-13 200. (doi:10.1523/JNEUROSCI.3315-10.2010)
149. Carandini M. 2012 From circuits to behavior: a bridge too far? Nat. Neurosci. 15, 507-509. (doi:10. 1038/nn.3043)
150. Gold JI, Shadlen MN. 2007 The neural basis of decision making. Annu. Rev. Neurosci. 30, 535-574. (doi:10.1146/annurev.neuro.29.051605. 113038)
151. Levin M, Pezzulo G, Finkelstein JM. In press. Endogenous bioelectric signaling networks: exploiting voltage gradients for control of growth and form. Ann. Rev. Biomed. Eng.